Data transformation caching in an artificial intelligence infrastructure

ABSTRACT

Data transformation caching in an artificial intelligence infrastructure that includes one or more storage systems and one or more graphical processing unit (‘GPU’) servers, including: identifying, in dependence upon one or more machine learning models to be executed on the GPU servers, one or more transformations to apply to a dataset; generating, in dependence upon the one or more transformations, a transformed dataset; storing, within one or more of the storage systems, the transformed dataset; receiving a plurality of requests to transmit the transformed dataset to one or more of the GPU servers; and responsive to each request, transmitting, from the one or more storage systems to the one or more GPU servers without re-performing the one or more transformations on the dataset, the transformed dataset.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a non-provisional application for patent entitled toa filing date and claiming the benefit of earlier-filed U.S. ProvisionalPatent Application Ser. No. 62/574,534, filed Oct. 19, 2017, U.S.Provisional Patent Application Ser. No. 62/576,523, filed Oct. 24, 2017,U.S. Provisional Patent Application Ser. No. 62/620,286, filed Jan. 22,2018, U.S. Provisional Patent Application Ser. No. 62/648,368, filedMar. 26, 2018, and U.S. Provisional Patent Application Ser. No.62/650,736, filed Mar. 30, 2018.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1A illustrates a first example system for data storage inaccordance with some implementations.

FIG. 1B illustrates a second example system for data storage inaccordance with some implementations.

FIG. 1C illustrates a third example system for data storage inaccordance with some implementations.

FIG. 1D illustrates a fourth example system for data storage inaccordance with some implementations.

FIG. 2A is a perspective view of a storage cluster with multiple storagenodes and internal storage coupled to each storage node to providenetwork attached storage, in accordance with some embodiments.

FIG. 2B is a block diagram showing an interconnect switch couplingmultiple storage nodes in accordance with some embodiments.

FIG. 2C is a multiple level block diagram, showing contents of a storagenode and contents of one of the non-volatile solid state storage unitsin accordance with some embodiments.

FIG. 2D shows a storage server environment, which uses embodiments ofthe storage nodes and storage units of some previous figures inaccordance with some embodiments.

FIG. 2E is a blade hardware block diagram, showing a control plane,compute and storage planes, and authorities interacting with underlyingphysical resources, in accordance with some embodiments.

FIG. 2F depicts elasticity software layers in blades of a storagecluster, in accordance with some embodiments.

FIG. 2G depicts authorities and storage resources in blades of a storagecluster, in accordance with some embodiments.

FIG. 3A sets forth a diagram of a storage system that is coupled fordata communications with a cloud services provider in accordance withsome embodiments of the present disclosure.

FIG. 3B sets forth a diagram of a storage system in accordance with someembodiments of the present disclosure.

FIG. 4 sets forth a flow chart illustrating an example method forexecuting a big data analytics pipeline in a storage system thatincludes compute resources and shared storage resources according tosome embodiments of the present disclosure.

FIG. 5 sets forth a flow chart illustrating an additional example methodfor executing a big data analytics pipeline in a storage system thatincludes compute resources and shared storage resources according tosome embodiments of the present disclosure.

FIG. 6 sets forth a flow chart illustrating an additional example methodfor executing a big data analytics pipeline in a storage system thatincludes compute resources and shared storage resources according tosome embodiments of the present disclosure.

FIG. 7 sets forth a flow chart illustrating an additional example methodfor executing a big data analytics pipeline in a storage system thatincludes compute resources and shared storage resources according tosome embodiments of the present disclosure.

FIG. 8A sets forth a diagram illustrating an example computerarchitecture for implementing an artificial intelligence and machinelearning infrastructure configured to fit within a single chassisaccording to some embodiments of the present disclosure.

FIG. 8B sets forth a flow chart illustrating an additional examplemethod for executing a big data analytics pipeline in a storage systemthat includes compute resources and shared storage resources accordingto some embodiments of the present disclosure.

FIG. 9 sets forth a flow chart illustrating an additional example methodfor executing a big data analytics pipeline in a storage system thatincludes compute resources and shared storage resources according tosome embodiments of the present disclosure.

FIG. 10 sets forth a flow chart illustrating an additional examplemethod for executing a big data analytics pipeline in a storage systemthat includes compute resources and shared storage resources accordingto some embodiments of the present disclosure.

FIG. 11A sets forth a diagram illustrating an example artificialintelligence and machine learning infrastructure according to someembodiments of the present disclosure.

FIG. 11B sets forth a diagram illustrating an example computerarchitecture for implementing an artificial intelligence and machinelearning infrastructure within a single chassis according to someembodiments of the present disclosure.

FIG. 11C sets forth a diagram illustrating an example implementation ofan artificial intelligence and machine learning infrastructure softwarestack according to some embodiments of the present disclosure.

FIG. 11D sets forth a flow chart illustrating an example method forinterconnecting a graphical processing unit layer and a storage layer ofan artificial intelligence and machine learning infrastructure accordingto some embodiments of the present disclosure.

FIG. 12A sets forth a flow chart illustrating an example method ofmonitoring an artificial intelligence and machine learninginfrastructure according to some embodiments of the present disclosure.

FIG. 12B sets forth a flow chart illustrating an example method ofoptimizing an artificial intelligence and machine learninginfrastructure according to some embodiments of the present disclosure.

FIG. 13 sets forth a flow chart illustrating an example method of datatransformation caching in an artificial intelligence infrastructure thatincludes one or more storage systems and one or more GPU serversaccording to some embodiments of the present disclosure.

FIG. 14 sets forth a flow chart illustrating an additional examplemethod of data transformation caching in an artificial intelligenceinfrastructure that includes one or more storage systems and one or moreGPU servers according to some embodiments of the present disclosure.

FIG. 15 sets forth a flow chart illustrating an example method of datatransformation caching in an artificial intelligence infrastructure thatincludes one or more storage systems and one or more GPU serversaccording to some embodiments of the present disclosure.

FIG. 16 sets forth a flow chart illustrating an example method of datatransformation caching in an artificial intelligence infrastructure thatincludes one or more storage systems and one or more GPU serversaccording to some embodiments of the present disclosure.

DESCRIPTION OF EMBODIMENTS

Example methods, apparatuses, and products for data transformationcaching in an artificial intelligence infrastructure in accordance withembodiments of the present disclosure are described with reference tothe accompanying drawings, beginning with FIG. 1A. FIG. 1A illustratesan example system for data storage, in accordance with someimplementations. System 100 (also referred to as “storage system”herein) includes numerous elements for purposes of illustration ratherthan limitation. It may be noted that system 100 may include the same,more, or fewer elements configured in the same or different manner inother implementations.

System 100 includes a number of computing devices 164A-B. Computingdevices (also referred to as “client devices” herein) may be embodied,for example, a server in a data center, a workstation, a personalcomputer, a notebook, or the like. Computing devices 164A-B may becoupled for data communications to one or more storage arrays 102A-Bthrough a storage area network (‘SAN’) 158 or a local area network(‘LAN’) 160.

The SAN 158 may be implemented with a variety of data communicationsfabrics, devices, and protocols. For example, the fabrics for SAN 158may include Fibre Channel, Ethernet, Infiniband, Serial Attached SmallComputer System Interface (‘SAS’), or the like. Data communicationsprotocols for use with SAN 158 may include Advanced TechnologyAttachment (‘ATA’), Fibre Channel Protocol, Small Computer SystemInterface (‘SCSI’), Internet Small Computer System Interface (‘iSCSI’),HyperSCSI, Non-Volatile Memory Express (‘NVMe’) over Fabrics, or thelike. It may be noted that SAN 158 is provided for illustration, ratherthan limitation. Other data communication couplings may be implementedbetween computing devices 164A-B and storage arrays 102A-B.

The LAN 160 may also be implemented with a variety of fabrics, devices,and protocols. For example, the fabrics for LAN 160 may include Ethernet(802.3), wireless (802.11), or the like. Data communication protocolsfor use in LAN 160 may include Transmission Control Protocol (‘TCP’),User Datagram Protocol (‘UDP’), Internet Protocol (IF), HyperTextTransfer Protocol (‘HTTP’), Wireless Access Protocol (‘WAP’), HandheldDevice Transport Protocol (‘HDTP’), Session Initiation Protocol (‘SIP’),Real Time Protocol (‘RTP’), or the like.

Storage arrays 102A-B may provide persistent data storage for thecomputing devices 164A-B. Storage array 102A may be contained in achassis (not shown), and storage array 102B may be contained in anotherchassis (not shown), in implementations. Storage array 102A and 102B mayinclude one or more storage array controllers 110A-D (also referred toas “controller” herein). A storage array controller 110A-D may beembodied as a module of automated computing machinery comprisingcomputer hardware, computer software, or a combination of computerhardware and software. In some implementations, the storage arraycontrollers 110A-D may be configured to carry out various storage tasks.Storage tasks may include writing data received from the computingdevices 164A-B to storage array 102A-B, erasing data from storage array102A-B, retrieving data from storage array 102A-B and providing data tocomputing devices 164A-B, monitoring and reporting of disk utilizationand performance, performing redundancy operations, such as RedundantArray of Independent Drives (‘RAID’) or RAID-like data redundancyoperations, compressing data, encrypting data, and so forth.

Storage array controller 110A-D may be implemented in a variety of ways,including as a Field Programmable Gate Array (‘FPGA’), a ProgrammableLogic Chip (‘PLC’), an Application Specific Integrated Circuit (‘ASIC’),System-on-Chip (‘SOC’), or any computing device that includes discretecomponents such as a processing device, central processing unit,computer memory, or various adapters. Storage array controller 110A-Dmay include, for example, a data communications adapter configured tosupport communications via the SAN 158 or LAN 160. In someimplementations, storage array controller 110A-D may be independentlycoupled to the LAN 160. In implementations, storage array controller110A-D may include an I/O controller or the like that couples thestorage array controller 110A-D for data communications, through amidplane (not shown), to a persistent storage resource 170A-B (alsoreferred to as a “storage resource” herein). The persistent storageresource 170A-B main include any number of storage drives 171A-F (alsoreferred to as “storage devices” herein) and any number of non-volatileRandom Access Memory (‘NVRAM’) devices (not shown).

In some implementations, the NVRAM devices of a persistent storageresource 170A-B may be configured to receive, from the storage arraycontroller 110A-D, data to be stored in the storage drives 171A-F. Insome examples, the data may originate from computing devices 164A-B. Insome examples, writing data to the NVRAM device may be carried out morequickly than directly writing data to the storage drive 171A-F. Inimplementations, the storage array controller 110A-D may be configuredto utilize the NVRAM devices as a quickly accessible buffer for datadestined to be written to the storage drives 171A-F. Latency for writerequests using NVRAM devices as a buffer may be improved relative to asystem in which a storage array controller 110A-D writes data directlyto the storage drives 171A-F. In some implementations, the NVRAM devicesmay be implemented with computer memory in the form of high bandwidth,low latency RAM. The NVRAM device is referred to as “non-volatile”because the NVRAM device may receive or include a unique power sourcethat maintains the state of the RAM after main power loss to the NVRAMdevice. Such a power source may be a battery, one or more capacitors, orthe like. In response to a power loss, the NVRAM device may beconfigured to write the contents of the RAM to a persistent storage,such as the storage drives 171A-F.

In implementations, storage drive 171A-F may refer to any deviceconfigured to record data persistently, where “persistently” or“persistent” refers as to a device's ability to maintain recorded dataafter loss of power. In some implementations, storage drive 171A-F maycorrespond to non-disk storage media. For example, the storage drive171A-F may be one or more solid-state drives (‘SSDs’), flash memorybased storage, any type of solid-state non-volatile memory, or any othertype of non-mechanical storage device. In other implementations, storagedrive 171A-F may include may include mechanical or spinning hard disk,such as hard-disk drives (‘HDD’).

In some implementations, the storage array controllers 110A-D may beconfigured for offloading device management responsibilities fromstorage drive 171A-F in storage array 102A-B. For example, storage arraycontrollers 110A-D may manage control information that may describe thestate of one or more memory blocks in the storage drives 171A-F. Thecontrol information may indicate, for example, that a particular memoryblock has failed and should no longer be written to, that a particularmemory block contains boot code for a storage array controller 110A-D,the number of program-erase (‘P/E’) cycles that have been performed on aparticular memory block, the age of data stored in a particular memoryblock, the type of data that is stored in a particular memory block, andso forth. In some implementations, the control information may be storedwith an associated memory block as metadata. In other implementations,the control information for the storage drives 171A-F may be stored inone or more particular memory blocks of the storage drives 171A-F thatare selected by the storage array controller 110A-D. The selected memoryblocks may be tagged with an identifier indicating that the selectedmemory block contains control information. The identifier may beutilized by the storage array controllers 110A-D in conjunction withstorage drives 171A-F to quickly identify the memory blocks that containcontrol information. For example, the storage controllers 110A-D mayissue a command to locate memory blocks that contain controlinformation. It may be noted that control information may be so largethat parts of the control information may be stored in multiplelocations, that the control information may be stored in multiplelocations for purposes of redundancy, for example, or that the controlinformation may otherwise be distributed across multiple memory blocksin the storage drive 171A-F.

In implementations, storage array controllers 110A-D may offload devicemanagement responsibilities from storage drives 171A-F of storage array102A-B by retrieving, from the storage drives 171A-F, controlinformation describing the state of one or more memory blocks in thestorage drives 171A-F. Retrieving the control information from thestorage drives 171A-F may be carried out, for example, by the storagearray controller 110A-D querying the storage drives 171A-F for thelocation of control information for a particular storage drive 171A-F.The storage drives 171A-F may be configured to execute instructions thatenable the storage drive 171A-F to identify the location of the controlinformation. The instructions may be executed by a controller (notshown) associated with or otherwise located on the storage drive 171A-Fand may cause the storage drive 171A-F to scan a portion of each memoryblock to identify the memory blocks that store control information forthe storage drives 171A-F. The storage drives 171A-F may respond bysending a response message to the storage array controller 110A-D thatincludes the location of control information for the storage drive171A-F. Responsive to receiving the response message, storage arraycontrollers 110A-D may issue a request to read data stored at theaddress associated with the location of control information for thestorage drives 171A-F.

In other implementations, the storage array controllers 110A-D mayfurther offload device management responsibilities from storage drives171A-F by performing, in response to receiving the control information,a storage drive management operation. A storage drive managementoperation may include, for example, an operation that is typicallyperformed by the storage drive 171A-F (e.g., the controller (not shown)associated with a particular storage drive 171A-F). A storage drivemanagement operation may include, for example, ensuring that data is notwritten to failed memory blocks within the storage drive 171A-F,ensuring that data is written to memory blocks within the storage drive171A-F in such a way that adequate wear leveling is achieved, and soforth.

In implementations, storage array 102A-B may implement two or morestorage array controllers 110A-D. For example, storage array 102A mayinclude storage array controllers 110A and storage array controllers110B. At a given instance, a single storage array controller 110A-D(e.g., storage array controller 110A) of a storage system 100 may bedesignated with primary status (also referred to as “primary controller”herein), and other storage array controllers 110A-D (e.g., storage arraycontroller 110A) may be designated with secondary status (also referredto as “secondary controller” herein). The primary controller may haveparticular rights, such as permission to alter data in persistentstorage resource 170A-B (e.g., writing data to persistent storageresource 170A-B). At least some of the rights of the primary controllermay supersede the rights of the secondary controller. For instance, thesecondary controller may not have permission to alter data in persistentstorage resource 170A-B when the primary controller has the right. Thestatus of storage array controllers 110A-D may change. For example,storage array controller 110A may be designated with secondary status,and storage array controller 110B may be designated with primary status.

In some implementations, a primary controller, such as storage arraycontroller 110A, may serve as the primary controller for one or morestorage arrays 102A-B, and a second controller, such as storage arraycontroller 110B, may serve as the secondary controller for the one ormore storage arrays 102A-B. For example, storage array controller 110Amay be the primary controller for storage array 102A and storage array102B, and storage array controller 110B may be the secondary controllerfor storage array 102A and 102B. In some implementations, storage arraycontrollers 110C and 110D (also referred to as “storage processingmodules”) may neither have primary or secondary status. Storage arraycontrollers 110C and 110D, implemented as storage processing modules,may act as a communication interface between the primary and secondarycontrollers (e.g., storage array controllers 110A and 110B,respectively) and storage array 102B. For example, storage arraycontroller 110A of storage array 102A may send a write request, via SAN158, to storage array 102B. The write request may be received by bothstorage array controllers 110C and 110D of storage array 102B. Storagearray controllers 110C and 110D facilitate the communication, e.g., sendthe write request to the appropriate storage drive 171A-F. It may benoted that in some implementations storage processing modules may beused to increase the number of storage drives controlled by the primaryand secondary controllers.

In implementations, storage array controllers 110A-D are communicativelycoupled, via a midplane (not shown), to one or more storage drives171A-F and to one or more NVRAM devices (not shown) that are included aspart of a storage array 102A-B. The storage array controllers 110A-D maybe coupled to the midplane via one or more data communication links andthe midplane may be coupled to the storage drives 171A-F and the NVRAMdevices via one or more data communications links. The datacommunications links described herein are collectively illustrated bydata communications links 108A-D and may include a Peripheral ComponentInterconnect Express (‘PCIe’) bus, for example.

FIG. 1B illustrates an example system for data storage, in accordancewith some implementations. Storage array controller 101 illustrated inFIG. 1B may similar to the storage array controllers 110A-D describedwith respect to FIG. 1A. In one example, storage array controller 101may be similar to storage array controller 110A or storage arraycontroller 110B. Storage array controller 101 includes numerous elementsfor purposes of illustration rather than limitation. It may be notedthat storage array controller 101 may include the same, more, or fewerelements configured in the same or different manner in otherimplementations. It may be noted that elements of FIG. 1A may beincluded below to help illustrate features of storage array controller101.

Storage array controller 101 may include one or more processing devices104 and random access memory (‘RAM’) 111. Processing device 104 (orcontroller 101) represents one or more general-purpose processingdevices such as a microprocessor, central processing unit, or the like.More particularly, the processing device 104 (or controller 101) may bea complex instruction set computing (‘CISC’) microprocessor, reducedinstruction set computing (‘RISC’) microprocessor, very long instructionword (‘VLIW’) microprocessor, or a processor implementing otherinstruction sets or processors implementing a combination of instructionsets. The processing device 104 (or controller 101) may also be one ormore special-purpose processing devices such as an application specificintegrated circuit (‘ASIC’), a field programmable gate array (‘FPGA’), adigital signal processor (‘DSP’), network processor, or the like.

The processing device 104 may be connected to the RAM 111 via a datacommunications link 106, which may be embodied as a high speed memorybus such as a Double-Data Rate 4 (‘DDR4’) bus. Stored in RAM 111 is anoperating system 112. In some implementations, instructions 113 arestored in RAM 111. Instructions 113 may include computer programinstructions for performing operations in in a direct-mapped flashstorage system. In one embodiment, a direct-mapped flash storage systemis one that that addresses data blocks within flash drives directly andwithout an address translation performed by the storage controllers ofthe flash drives.

In implementations, storage array controller 101 includes one or morehost bus adapters 103A-C that are coupled to the processing device 104via a data communications link 105A-C. In implementations, host busadapters 103A-C may be computer hardware that connects a host system(e.g., the storage array controller) to other network and storagearrays. In some examples, host bus adapters 103A-C may be a FibreChannel adapter that enables the storage array controller 101 to connectto a SAN, an Ethernet adapter that enables the storage array controller101 to connect to a LAN, or the like. Host bus adapters 103A-C may becoupled to the processing device 104 via a data communications link105A-C such as, for example, a PCIe bus.

In implementations, storage array controller 101 may include a host busadapter 114 that is coupled to an expander 115. The expander 115 may beused to attach a host system to a larger number of storage drives. Theexpander 115 may, for example, be a SAS expander utilized to enable thehost bus adapter 114 to attach to storage drives in an implementationwhere the host bus adapter 114 is embodied as a SAS controller.

In implementations, storage array controller 101 may include a switch116 coupled to the processing device 104 via a data communications link109. The switch 116 may be a computer hardware device that can createmultiple endpoints out of a single endpoint, thereby enabling multipledevices to share a single endpoint. The switch 116 may, for example, bea PCIe switch that is coupled to a PCIe bus (e.g., data communicationslink 109) and presents multiple PCIe connection points to the midplane.

In implementations, storage array controller 101 includes a datacommunications link 107 for coupling the storage array controller 101 toother storage array controllers. In some examples, data communicationslink 107 may be a QuickPath Interconnect (QPI) interconnect.

A traditional storage system that uses traditional flash drives mayimplement a process across the flash drives that are part of thetraditional storage system. For example, a higher level process of thestorage system may initiate and control a process across the flashdrives. However, a flash drive of the traditional storage system mayinclude its own storage controller that also performs the process. Thus,for the traditional storage system, a higher level process (e.g.,initiated by the storage system) and a lower level process (e.g.,initiated by a storage controller of the storage system) may both beperformed.

To resolve various deficiencies of a traditional storage system,operations may be performed by higher level processes and not by thelower level processes. For example, the flash storage system may includeflash drives that do not include storage controllers that provide theprocess. Thus, the operating system of the flash storage system itselfmay initiate and control the process. This may be accomplished by adirect-mapped flash storage system that addresses data blocks within theflash drives directly and without an address translation performed bythe storage controllers of the flash drives.

The operating system of the flash storage system may identify andmaintain a list of allocation units across multiple flash drives of theflash storage system. The allocation units may be entire erase blocks ormultiple erase blocks. The operating system may maintain a map oraddress range that directly maps addresses to erase blocks of the flashdrives of the flash storage system.

Direct mapping to the erase blocks of the flash drives may be used torewrite data and erase data. For example, the operations may beperformed on one or more allocation units that include a first data anda second data where the first data is to be retained and the second datais no longer being used by the flash storage system. The operatingsystem may initiate the process to write the first data to new locationswithin other allocation units and erasing the second data and markingthe allocation units as being available for use for subsequent data.Thus, the process may only be performed by the higher level operatingsystem of the flash storage system without an additional lower levelprocess being performed by controllers of the flash drives.

Advantages of the process being performed only by the operating systemof the flash storage system include increased reliability of the flashdrives of the flash storage system as unnecessary or redundant writeoperations are not being performed during the process. One possiblepoint of novelty here is the concept of initiating and controlling theprocess at the operating system of the flash storage system. Inaddition, the process can be controlled by the operating system acrossmultiple flash drives. This is contrast to the process being performedby a storage controller of a flash drive.

A storage system can consist of two storage array controllers that sharea set of drives for failover purposes, or it could consist of a singlestorage array controller that provides a storage service that utilizesmultiple drives, or it could consist of a distributed network of storagearray controllers each with some number of drives or some amount ofFlash storage where the storage array controllers in the networkcollaborate to provide a complete storage service and collaborate onvarious aspects of a storage service including storage allocation andgarbage collection.

FIG. 1C illustrates a third example system 117 for data storage inaccordance with some implementations. System 117 (also referred to as“storage system” herein) includes numerous elements for purposes ofillustration rather than limitation. It may be noted that system 117 mayinclude the same, more, or fewer elements configured in the same ordifferent manner in other implementations.

In one embodiment, system 117 includes a dual Peripheral ComponentInterconnect (‘PCI’) flash storage device 118 with separatelyaddressable fast write storage. System 117 may include a storagecontroller 119. In one embodiment, storage controller 119A-D may be aCPU, ASIC, FPGA, or any other circuitry that may implement controlstructures necessary according to the present disclosure. In oneembodiment, system 117 includes flash memory devices (e.g., includingflash memory devices 120 a-n), operatively coupled to various channelsof the storage device controller 119. Flash memory devices 120 a-n, maybe presented to the controller 119A-D as an addressable collection ofFlash pages, erase blocks, and/or control elements sufficient to allowthe storage device controller 119A-D to program and retrieve variousaspects of the Flash. In one embodiment, storage device controller119A-D may perform operations on flash memory devices 120A-N includingstoring and retrieving data content of pages, arranging and erasing anyblocks, tracking statistics related to the use and reuse of Flash memorypages, erase blocks, and cells, tracking and predicting error codes andfaults within the Flash memory, controlling voltage levels associatedwith programming and retrieving contents of Flash cells, etc.

In one embodiment, system 117 may include RAM 121 to store separatelyaddressable fast-write data. In one embodiment, RAM 121 may be one ormore separate discrete devices. In another embodiment, RAM 121 may beintegrated into storage device controller 119A-D or multiple storagedevice controllers. The RAM 121 may be utilized for other purposes aswell, such as temporary program memory for a processing device (e.g., aCPU) in the storage device controller 119.

In one embodiment, system 119A-D may include a stored energy device 122,such as a rechargeable battery or a capacitor. Stored energy device 122may store energy sufficient to power the storage device controller 119,some amount of the RAM (e.g., RAM 121), and some amount of Flash memory(e.g., Flash memory 120 a-120 n) for sufficient time to write thecontents of RAM to Flash memory. In one embodiment, storage devicecontroller 119A-D may write the contents of RAM to Flash Memory if thestorage device controller detects loss of external power.

In one embodiment, system 117 includes two data communications links 123a, 123 b. In one embodiment, data communications links 123 a, 123 b maybe PCI interfaces. In another embodiment, data communications links 123a, 123 b may be based on other communications standards (e.g.,HyperTransport, InfiniBand, etc.). Data communications links 123 a, 123b may be based on non-volatile memory express (‘NVMe’) or NVMe overfabrics (‘NVMf’) specifications that allow external connection to thestorage device controller 119A-D from other components in the storagesystem 117. It should be noted that data communications links may beinterchangeably referred to herein as PCI buses for convenience.

System 117 may also include an external power source (not shown), whichmay be provided over one or both data communications links 123 a, 123 b,or which may be provided separately. An alternative embodiment includesa separate Flash memory (not shown) dedicated for use in storing thecontent of RAM 121. The storage device controller 119A-D may present alogical device over a PCI bus which may include an addressablefast-write logical device, or a distinct part of the logical addressspace of the storage device 118, which may be presented as PCI memory oras persistent storage. In one embodiment, operations to store into thedevice are directed into the RAM 121. On power failure, the storagedevice controller 119A-D may write stored content associated with theaddressable fast-write logical storage to Flash memory (e.g., Flashmemory 120 a-n) for long-term persistent storage.

In one embodiment, the logical device may include some presentation ofsome or all of the content of the Flash memory devices 120 a-n, wherethat presentation allows a storage system including a storage device 118(e.g., storage system 117) to directly address Flash memory pages anddirectly reprogram erase blocks from storage system components that areexternal to the storage device through the PCI bus. The presentation mayalso allow one or more of the external components to control andretrieve other aspects of the Flash memory including some or all of:tracking statistics related to use and reuse of Flash memory pages,erase blocks, and cells across all the Flash memory devices; trackingand predicting error codes and faults within and across the Flash memorydevices; controlling voltage levels associated with programming andretrieving contents of Flash cells; etc.

In one embodiment, the stored energy device 122 may be sufficient toensure completion of in-progress operations to the Flash memory devices107 a-120 n stored energy device 122 may power storage device controller119A-D and associated Flash memory devices (e.g., 120 a-n) for thoseoperations, as well as for the storing of fast-write RAM to Flashmemory. Stored energy device 122 may be used to store accumulatedstatistics and other parameters kept and tracked by the Flash memorydevices 120 a-n and/or the storage device controller 119. Separatecapacitors or stored energy devices (such as smaller capacitors near orembedded within the Flash memory devices themselves) may be used forsome or all of the operations described herein.

Various schemes may be used to track and optimize the life span of thestored energy component, such as adjusting voltage levels over time,partially discharging the storage energy device 122 to measurecorresponding discharge characteristics, etc. If the available energydecreases over time, the effective available capacity of the addressablefast-write storage may be decreased to ensure that it can be writtensafely based on the currently available stored energy.

FIG. 1D illustrates a third example system 124 for data storage inaccordance with some implementations. In one embodiment, system 124includes storage controllers 125 a, 125 b. In one embodiment, storagecontrollers 125 a, 125 b are operatively coupled to Dual PCI storagedevices 119 a, 119 b and 119 c, 119 d, respectively. Storage controllers125 a, 125 b may be operatively coupled (e.g., via a storage network130) to some number of host computers 127 a-n.

In one embodiment, two storage controllers (e.g., 125 a and 125 b)provide storage services, such as a SCS) block storage array, a fileserver, an object server, a database or data analytics service, etc. Thestorage controllers 125 a, 125 b may provide services through somenumber of network interfaces (e.g., 126 a-d) to host computers 127 a-noutside of the storage system 124. Storage controllers 125 a, 125 b mayprovide integrated services or an application entirely within thestorage system 124, forming a converged storage and compute system. Thestorage controllers 125 a, 125 b may utilize the fast write memorywithin or across storage devices 119 a-d to journal in progressoperations to ensure the operations are not lost on a power failure,storage controller removal, storage controller or storage systemshutdown, or some fault of one or more software or hardware componentswithin the storage system 124.

In one embodiment, controllers 125 a, 125 b operate as PCI masters toone or the other PCI buses 128 a, 128 b. In another embodiment, 128 aand 128 b may be based on other communications standards (e.g.,HyperTransport, InfiniBand, etc.). Other storage system embodiments mayoperate storage controllers 125 a, 125 b as multi-masters for both PCIbuses 128 a, 128 b. Alternately, a PCI/NVMe/NVMf switchinginfrastructure or fabric may connect multiple storage controllers. Somestorage system embodiments may allow storage devices to communicate witheach other directly rather than communicating only with storagecontrollers. In one embodiment, a storage device controller 119 a may beoperable under direction from a storage controller 125 a to synthesizeand transfer data to be stored into Flash memory devices from data thathas been stored in RAM (e.g., RAM 121 of FIG. 1C). For example, arecalculated version of RAM content may be transferred after a storagecontroller has determined that an operation has fully committed acrossthe storage system, or when fast-write memory on the device has reacheda certain used capacity, or after a certain amount of time, to ensureimprove safety of the data or to release addressable fast-write capacityfor reuse. This mechanism may be used, for example, to avoid a secondtransfer over a bus (e.g., 128 a, 128 b) from the storage controllers125 a, 125 b. In one embodiment, a recalculation may include compressingdata, attaching indexing or other metadata, combining multiple datasegments together, performing erasure code calculations, etc.

In one embodiment, under direction from a storage controller 125 a, 125b, a storage device controller 119 a, 119 b may be operable to calculateand transfer data to other storage devices from data stored in RAM(e.g., RAM 121 of FIG. 1C) without involvement of the storagecontrollers 125 a, 125 b. This operation may be used to mirror datastored in one controller 125 a to another controller 125 b, or it couldbe used to offload compression, data aggregation, and/or erasure codingcalculations and transfers to storage devices to reduce load on storagecontrollers or the storage controller interface 129 a, 129 b to the PCIbus 128 a, 128 b.

A storage device controller 119A-D may include mechanisms forimplementing high availability primitives for use by other parts of astorage system external to the Dual PCI storage device 118. For example,reservation or exclusion primitives may be provided so that, in astorage system with two storage controllers providing a highly availablestorage service, one storage controller may prevent the other storagecontroller from accessing or continuing to access the storage device.This could be used, for example, in cases where one controller detectsthat the other controller is not functioning properly or where theinterconnect between the two storage controllers may itself not befunctioning properly.

In one embodiment, a storage system for use with Dual PCI direct mappedstorage devices with separately addressable fast write storage includessystems that manage erase blocks or groups of erase blocks as allocationunits for storing data on behalf of the storage service, or for storingmetadata (e.g., indexes, logs, etc.) associated with the storageservice, or for proper management of the storage system itself. Flashpages, which may be a few kilobytes in size, may be written as dataarrives or as the storage system is to persist data for long intervalsof time (e.g., above a defined threshold of time). To commit data morequickly, or to reduce the number of writes to the Flash memory devices,the storage controllers may first write data into the separatelyaddressable fast write storage on one more storage devices.

In one embodiment, the storage controllers 125 a, 125 b may initiate theuse of erase blocks within and across storage devices (e.g., 118) inaccordance with an age and expected remaining lifespan of the storagedevices, or based on other statistics. The storage controllers 125 a,125 b may initiate garbage collection and data migration data betweenstorage devices in accordance with pages that are no longer needed aswell as to manage Flash page and erase block lifespans and to manageoverall system performance.

In one embodiment, the storage system 124 may utilize mirroring and/orerasure coding schemes as part of storing data into addressable fastwrite storage and/or as part of writing data into allocation unitsassociated with erase blocks. Erasure codes may be used across storagedevices, as well as within erase blocks or allocation units, or withinand across Flash memory devices on a single storage device, to provideredundancy against single or multiple storage device failures or toprotect against internal corruptions of Flash memory pages resultingfrom Flash memory operations or from degradation of Flash memory cells.Mirroring and erasure coding at various levels may be used to recoverfrom multiple types of failures that occur separately or in combination.

The embodiments depicted with reference to FIGS. 2A-G illustrate astorage cluster that stores user data, such as user data originatingfrom one or more user or client systems or other sources external to thestorage cluster. The storage cluster distributes user data acrossstorage nodes housed within a chassis, or across multiple chassis, usingerasure coding and redundant copies of metadata. Erasure coding refersto a method of data protection or reconstruction in which data is storedacross a set of different locations, such as disks, storage nodes orgeographic locations. Flash memory is one type of solid-state memorythat may be integrated with the embodiments, although the embodimentsmay be extended to other types of solid-state memory or other storagemedium, including non-solid state memory. Control of storage locationsand workloads are distributed across the storage locations in aclustered peer-to-peer system. Tasks such as mediating communicationsbetween the various storage nodes, detecting when a storage node hasbecome unavailable, and balancing I/Os (inputs and outputs) across thevarious storage nodes, are all handled on a distributed basis. Data islaid out or distributed across multiple storage nodes in data fragmentsor stripes that support data recovery in some embodiments. Ownership ofdata can be reassigned within a cluster, independent of input and outputpatterns. This architecture described in more detail below allows astorage node in the cluster to fail, with the system remainingoperational, since the data can be reconstructed from other storagenodes and thus remain available for input and output operations. Invarious embodiments, a storage node may be referred to as a clusternode, a blade, or a server.

The storage cluster may be contained within a chassis, i.e., anenclosure housing one or more storage nodes. A mechanism to providepower to each storage node, such as a power distribution bus, and acommunication mechanism, such as a communication bus that enablescommunication between the storage nodes are included within the chassis.The storage cluster can run as an independent system in one locationaccording to some embodiments. In one embodiment, a chassis contains atleast two instances of both the power distribution and the communicationbus which may be enabled or disabled independently. The internalcommunication bus may be an Ethernet bus, however, other technologiessuch as PCIe, InfiniBand, and others, are equally suitable. The chassisprovides a port for an external communication bus for enablingcommunication between multiple chassis, directly or through a switch,and with client systems. The external communication may use a technologysuch as Ethernet, InfiniBand, Fibre Channel, etc. In some embodiments,the external communication bus uses different communication bustechnologies for inter-chassis and client communication. If a switch isdeployed within or between chassis, the switch may act as a translationbetween multiple protocols or technologies. When multiple chassis areconnected to define a storage cluster, the storage cluster may beaccessed by a client using either proprietary interfaces or standardinterfaces such as network file system (‘NFS’), common internet filesystem (‘CIFS’), small computer system interface (‘SCSI’) or hypertexttransfer protocol (‘HTTP’). Translation from the client protocol mayoccur at the switch, chassis external communication bus or within eachstorage node. In some embodiments, multiple chassis may be coupled orconnected to each other through an aggregator switch. A portion and/orall of the coupled or connected chassis may be designated as a storagecluster. As discussed above, each chassis can have multiple blades, eachblade has a media access control (‘MAC’) address, but the storagecluster is presented to an external network as having a single clusterIP address and a single MAC address in some embodiments.

Each storage node may be one or more storage servers and each storageserver is connected to one or more non-volatile solid state memoryunits, which may be referred to as storage units or storage devices. Oneembodiment includes a single storage server in each storage node andbetween one to eight non-volatile solid state memory units, however thisone example is not meant to be limiting. The storage server may includea processor, DRAM and interfaces for the internal communication bus andpower distribution for each of the power buses. Inside the storage node,the interfaces and storage unit share a communication bus, e.g., PCIExpress, in some embodiments. The non-volatile solid state memory unitsmay directly access the internal communication bus interface through astorage node communication bus, or request the storage node to accessthe bus interface. The non-volatile solid state memory unit contains anembedded CPU, solid state storage controller, and a quantity of solidstate mass storage, e.g., between 2-32 terabytes (‘TB’) in someembodiments. An embedded volatile storage medium, such as DRAM, and anenergy reserve apparatus are included in the non-volatile solid statememory unit. In some embodiments, the energy reserve apparatus is acapacitor, super-capacitor, or battery that enables transferring asubset of DRAM contents to a stable storage medium in the case of powerloss. In some embodiments, the non-volatile solid state memory unit isconstructed with a storage class memory, such as phase change ormagnetoresistive random access memory (‘MRAM’) that substitutes for DRAMand enables a reduced power hold-up apparatus.

One of many features of the storage nodes and non-volatile solid statestorage is the ability to proactively rebuild data in a storage cluster.The storage nodes and non-volatile solid state storage can determinewhen a storage node or non-volatile solid state storage in the storagecluster is unreachable, independent of whether there is an attempt toread data involving that storage node or non-volatile solid statestorage. The storage nodes and non-volatile solid state storage thencooperate to recover and rebuild the data in at least partially newlocations. This constitutes a proactive rebuild, in that the systemrebuilds data without waiting until the data is needed for a read accessinitiated from a client system employing the storage cluster. These andfurther details of the storage memory and operation thereof arediscussed below.

FIG. 2A is a perspective view of a storage cluster 161, with multiplestorage nodes 150 and internal solid-state memory coupled to eachstorage node to provide network attached storage or storage areanetwork, in accordance with some embodiments. A network attachedstorage, storage area network, or a storage cluster, or other storagememory, could include one or more storage clusters 161, each having oneor more storage nodes 150, in a flexible and reconfigurable arrangementof both the physical components and the amount of storage memoryprovided thereby. The storage cluster 161 is designed to fit in a rack,and one or more racks can be set up and populated as desired for thestorage memory. The storage cluster 161 has a chassis 138 havingmultiple slots 142. It should be appreciated that chassis 138 may bereferred to as a housing, enclosure, or rack unit. In one embodiment,the chassis 138 has fourteen slots 142, although other numbers of slotsare readily devised. For example, some embodiments have four slots,eight slots, sixteen slots, thirty-two slots, or other suitable numberof slots. Each slot 142 can accommodate one storage node 150 in someembodiments. Chassis 138 includes flaps 148 that can be utilized tomount the chassis 138 on a rack. Fans 144 provide air circulation forcooling of the storage nodes 150 and components thereof, although othercooling components could be used, or an embodiment could be devisedwithout cooling components. A switch fabric 146 couples storage nodes150 within chassis 138 together and to a network for communication tothe memory. In an embodiment depicted in herein, the slots 142 to theleft of the switch fabric 146 and fans 144 are shown occupied by storagenodes 150, while the slots 142 to the right of the switch fabric 146 andfans 144 are empty and available for insertion of storage node 150 forillustrative purposes. This configuration is one example, and one ormore storage nodes 150 could occupy the slots 142 in various furtherarrangements. The storage node arrangements need not be sequential oradjacent in some embodiments. Storage nodes 150 are hot pluggable,meaning that a storage node 150 can be inserted into a slot 142 in thechassis 138, or removed from a slot 142, without stopping or poweringdown the system. Upon insertion or removal of storage node 150 from slot142, the system automatically reconfigures in order to recognize andadapt to the change. Reconfiguration, in some embodiments, includesrestoring redundancy and/or rebalancing data or load.

Each storage node 150 can have multiple components. In the embodimentshown here, the storage node 150 includes a printed circuit board 159populated by a CPU 156, i.e., processor, a memory 154 coupled to the CPU156, and a non-volatile solid state storage 152 coupled to the CPU 156,although other mountings and/or components could be used in furtherembodiments. The memory 154 has instructions which are executed by theCPU 156 and/or data operated on by the CPU 156. As further explainedbelow, the non-volatile solid state storage 152 includes flash or, infurther embodiments, other types of solid-state memory.

Referring to FIG. 2A, storage cluster 161 is scalable, meaning thatstorage capacity with non-uniform storage sizes is readily added, asdescribed above. One or more storage nodes 150 can be plugged into orremoved from each chassis and the storage cluster self-configures insome embodiments. Plug-in storage nodes 150, whether installed in achassis as delivered or later added, can have different sizes. Forexample, in one embodiment a storage node 150 can have any multiple of 4TB, e.g., 8 TB, 12 TB, 16 TB, 32 TB, etc. In further embodiments, astorage node 150 could have any multiple of other storage amounts orcapacities. Storage capacity of each storage node 150 is broadcast, andinfluences decisions of how to stripe the data. For maximum storageefficiency, an embodiment can self-configure as wide as possible in thestripe, subject to a predetermined requirement of continued operationwith loss of up to one, or up to two, non-volatile solid state storageunits 152 or storage nodes 150 within the chassis.

FIG. 2B is a block diagram showing a communications interconnect 173 andpower distribution bus 172 coupling multiple storage nodes 150.Referring back to FIG. 2A, the communications interconnect 173 can beincluded in or implemented with the switch fabric 146 in someembodiments. Where multiple storage clusters 161 occupy a rack, thecommunications interconnect 173 can be included in or implemented with atop of rack switch, in some embodiments. As illustrated in FIG. 2B,storage cluster 161 is enclosed within a single chassis 138. Externalport 176 is coupled to storage nodes 150 through communicationsinterconnect 173, while external port 174 is coupled directly to astorage node. External power port 178 is coupled to power distributionbus 172. Storage nodes 150 may include varying amounts and differingcapacities of non-volatile solid state storage 152 as described withreference to FIG. 2A. In addition, one or more storage nodes 150 may bea compute only storage node as illustrated in FIG. 2B. Authorities 168are implemented on the non-volatile solid state storages 152, forexample as lists or other data structures stored in memory. In someembodiments the authorities are stored within the non-volatile solidstate storage 152 and supported by software executing on a controller orother processor of the non-volatile solid state storage 152. In afurther embodiment, authorities 168 are implemented on the storage nodes150, for example as lists or other data structures stored in the memory154 and supported by software executing on the CPU 156 of the storagenode 150. Authorities 168 control how and where data is stored in thenon-volatile solid state storages 152 in some embodiments. This controlassists in determining which type of erasure coding scheme is applied tothe data, and which storage nodes 150 have which portions of the data.Each authority 168 may be assigned to a non-volatile solid state storage152. Each authority may control a range of inode numbers, segmentnumbers, or other data identifiers which are assigned to data by a filesystem, by the storage nodes 150, or by the non-volatile solid statestorage 152, in various embodiments.

Every piece of data, and every piece of metadata, has redundancy in thesystem in some embodiments. In addition, every piece of data and everypiece of metadata has an owner, which may be referred to as anauthority. If that authority is unreachable, for example through failureof a storage node, there is a plan of succession for how to find thatdata or that metadata. In various embodiments, there are redundantcopies of authorities 168. Authorities 168 have a relationship tostorage nodes 150 and non-volatile solid state storage 152 in someembodiments. Each authority 168, covering a range of data segmentnumbers or other identifiers of the data, may be assigned to a specificnon-volatile solid state storage 152. In some embodiments theauthorities 168 for all of such ranges are distributed over thenon-volatile solid state storages 152 of a storage cluster. Each storagenode 150 has a network port that provides access to the non-volatilesolid state storage(s) 152 of that storage node 150. Data can be storedin a segment, which is associated with a segment number and that segmentnumber is an indirection for a configuration of a RAID (redundant arrayof independent disks) stripe in some embodiments. The assignment and useof the authorities 168 thus establishes an indirection to data.Indirection may be referred to as the ability to reference dataindirectly, in this case via an authority 168, in accordance with someembodiments. A segment identifies a set of non-volatile solid statestorage 152 and a local identifier into the set of non-volatile solidstate storage 152 that may contain data. In some embodiments, the localidentifier is an offset into the device and may be reused sequentiallyby multiple segments. In other embodiments the local identifier isunique for a specific segment and never reused. The offsets in thenon-volatile solid state storage 152 are applied to locating data forwriting to or reading from the non-volatile solid state storage 152 (inthe form of a RAID stripe). Data is striped across multiple units ofnon-volatile solid state storage 152, which may include or be differentfrom the non-volatile solid state storage 152 having the authority 168for a particular data segment.

If there is a change in where a particular segment of data is located,e.g., during a data move or a data reconstruction, the authority 168 forthat data segment should be consulted, at that non-volatile solid statestorage 152 or storage node 150 having that authority 168. In order tolocate a particular piece of data, embodiments calculate a hash valuefor a data segment or apply an inode number or a data segment number.The output of this operation points to a non-volatile solid statestorage 152 having the authority 168 for that particular piece of data.In some embodiments there are two stages to this operation. The firststage maps an entity identifier (ID), e.g., a segment number, inodenumber, or directory number to an authority identifier. This mapping mayinclude a calculation such as a hash or a bit mask. The second stage ismapping the authority identifier to a particular non-volatile solidstate storage 152, which may be done through an explicit mapping. Theoperation is repeatable, so that when the calculation is performed, theresult of the calculation repeatably and reliably points to a particularnon-volatile solid state storage 152 having that authority 168. Theoperation may include the set of reachable storage nodes as input. Ifthe set of reachable non-volatile solid state storage units changes theoptimal set changes. In some embodiments, the persisted value is thecurrent assignment (which is always true) and the calculated value isthe target assignment the cluster will attempt to reconfigure towards.This calculation may be used to determine the optimal non-volatile solidstate storage 152 for an authority in the presence of a set ofnon-volatile solid state storage 152 that are reachable and constitutethe same cluster. The calculation also determines an ordered set of peernon-volatile solid state storage 152 that will also record the authorityto non-volatile solid state storage mapping so that the authority may bedetermined even if the assigned non-volatile solid state storage isunreachable. A duplicate or substitute authority 168 may be consulted ifa specific authority 168 is unavailable in some embodiments.

With reference to FIGS. 2A and 2B, two of the many tasks of the CPU 156on a storage node 150 are to break up write data, and reassemble readdata. When the system has determined that data is to be written, theauthority 168 for that data is located as above. When the segment ID fordata is already determined the request to write is forwarded to thenon-volatile solid state storage 152 currently determined to be the hostof the authority 168 determined from the segment. The host CPU 156 ofthe storage node 150, on which the non-volatile solid state storage 152and corresponding authority 168 reside, then breaks up or shards thedata and transmits the data out to various non-volatile solid statestorage 152. The transmitted data is written as a data stripe inaccordance with an erasure coding scheme. In some embodiments, data isrequested to be pulled, and in other embodiments, data is pushed. Inreverse, when data is read, the authority 168 for the segment IDcontaining the data is located as described above. The host CPU 156 ofthe storage node 150 on which the non-volatile solid state storage 152and corresponding authority 168 reside requests the data from thenon-volatile solid state storage and corresponding storage nodes pointedto by the authority. In some embodiments the data is read from flashstorage as a data stripe. The host CPU 156 of storage node 150 thenreassembles the read data, correcting any errors (if present) accordingto the appropriate erasure coding scheme, and forwards the reassembleddata to the network. In further embodiments, some or all of these taskscan be handled in the non-volatile solid state storage 152. In someembodiments, the segment host requests the data be sent to storage node150 by requesting pages from storage and then sending the data to thestorage node making the original request.

In some systems, for example in UNIX-style file systems, data is handledwith an index node or inode, which specifies a data structure thatrepresents an object in a file system. The object could be a file or adirectory, for example. Metadata may accompany the object, as attributessuch as permission data and a creation timestamp, among otherattributes. A segment number could be assigned to all or a portion ofsuch an object in a file system. In other systems, data segments arehandled with a segment number assigned elsewhere. For purposes ofdiscussion, the unit of distribution is an entity, and an entity can bea file, a directory or a segment. That is, entities are units of data ormetadata stored by a storage system. Entities are grouped into setscalled authorities. Each authority has an authority owner, which is astorage node that has the exclusive right to update the entities in theauthority. In other words, a storage node contains the authority, andthat the authority, in turn, contains entities.

A segment is a logical container of data in accordance with someembodiments. A segment is an address space between medium address spaceand physical flash locations, i.e., the data segment number, are in thisaddress space. Segments may also contain meta-data, which enable dataredundancy to be restored (rewritten to different flash locations ordevices) without the involvement of higher level software. In oneembodiment, an internal format of a segment contains client data andmedium mappings to determine the position of that data. Each datasegment is protected, e.g., from memory and other failures, by breakingthe segment into a number of data and parity shards, where applicable.The data and parity shards are distributed, i.e., striped, acrossnon-volatile solid state storage 152 coupled to the host CPUs 156 (SeeFIGS. 2E and 2G) in accordance with an erasure coding scheme. Usage ofthe term segments refers to the container and its place in the addressspace of segments in some embodiments. Usage of the term stripe refersto the same set of shards as a segment and includes how the shards aredistributed along with redundancy or parity information in accordancewith some embodiments.

A series of address-space transformations takes place across an entirestorage system. At the top are the directory entries (file names) whichlink to an inode. Modes point into medium address space, where data islogically stored. Medium addresses may be mapped through a series ofindirect mediums to spread the load of large files, or implement dataservices like deduplication or snapshots. Medium addresses may be mappedthrough a series of indirect mediums to spread the load of large files,or implement data services like deduplication or snapshots. Segmentaddresses are then translated into physical flash locations. Physicalflash locations have an address range bounded by the amount of flash inthe system in accordance with some embodiments. Medium addresses andsegment addresses are logical containers, and in some embodiments use a128 bit or larger identifier so as to be practically infinite, with alikelihood of reuse calculated as longer than the expected life of thesystem. Addresses from logical containers are allocated in ahierarchical fashion in some embodiments. Initially, each non-volatilesolid state storage unit 152 may be assigned a range of address space.Within this assigned range, the non-volatile solid state storage 152 isable to allocate addresses without synchronization with othernon-volatile solid state storage 152.

Data and metadata is stored by a set of underlying storage layouts thatare optimized for varying workload patterns and storage devices. Theselayouts incorporate multiple redundancy schemes, compression formats andindex algorithms. Some of these layouts store information aboutauthorities and authority masters, while others store file metadata andfile data. The redundancy schemes include error correction codes thattolerate corrupted bits within a single storage device (such as a NANDflash chip), erasure codes that tolerate the failure of multiple storagenodes, and replication schemes that tolerate data center or regionalfailures. In some embodiments, low density parity check (‘LDPC’) code isused within a single storage unit. Reed-Solomon encoding is used withina storage cluster, and mirroring is used within a storage grid in someembodiments. Metadata may be stored using an ordered log structuredindex (such as a Log Structured Merge Tree), and large data may not bestored in a log structured layout.

In order to maintain consistency across multiple copies of an entity,the storage nodes agree implicitly on two things through calculations:(1) the authority that contains the entity, and (2) the storage nodethat contains the authority. The assignment of entities to authoritiescan be done by pseudo randomly assigning entities to authorities, bysplitting entities into ranges based upon an externally produced key, orby placing a single entity into each authority. Examples of pseudorandomschemes are linear hashing and the Replication Under Scalable Hashing(‘RUSH’) family of hashes, including Controlled Replication UnderScalable Hashing (‘CRUSH’). In some embodiments, pseudo-randomassignment is utilized only for assigning authorities to nodes becausethe set of nodes can change. The set of authorities cannot change so anysubjective function may be applied in these embodiments. Some placementschemes automatically place authorities on storage nodes, while otherplacement schemes rely on an explicit mapping of authorities to storagenodes. In some embodiments, a pseudorandom scheme is utilized to mapfrom each authority to a set of candidate authority owners. Apseudorandom data distribution function related to CRUSH may assignauthorities to storage nodes and create a list of where the authoritiesare assigned. Each storage node has a copy of the pseudorandom datadistribution function, and can arrive at the same calculation fordistributing, and later finding or locating an authority. Each of thepseudorandom schemes requires the reachable set of storage nodes asinput in some embodiments in order to conclude the same target nodes.Once an entity has been placed in an authority, the entity may be storedon physical devices so that no expected failure will lead to unexpecteddata loss. In some embodiments, rebalancing algorithms attempt to storethe copies of all entities within an authority in the same layout and onthe same set of machines.

Examples of expected failures include device failures, stolen machines,datacenter fires, and regional disasters, such as nuclear or geologicalevents. Different failures lead to different levels of acceptable dataloss. In some embodiments, a stolen storage node impacts neither thesecurity nor the reliability of the system, while depending on systemconfiguration, a regional event could lead to no loss of data, a fewseconds or minutes of lost updates, or even complete data loss.

In the embodiments, the placement of data for storage redundancy isindependent of the placement of authorities for data consistency. Insome embodiments, storage nodes that contain authorities do not containany persistent storage. Instead, the storage nodes are connected tonon-volatile solid state storage units that do not contain authorities.The communications interconnect between storage nodes and non-volatilesolid state storage units consists of multiple communicationtechnologies and has non-uniform performance and fault tolerancecharacteristics. In some embodiments, as mentioned above, non-volatilesolid state storage units are connected to storage nodes via PCIexpress, storage nodes are connected together within a single chassisusing Ethernet backplane, and chassis are connected together to form astorage cluster. Storage clusters are connected to clients usingEthernet or fiber channel in some embodiments. If multiple storageclusters are configured into a storage grid, the multiple storageclusters are connected using the Internet or other long-distancenetworking links, such as a “metro scale” link or private link that doesnot traverse the internet.

Authority owners have the exclusive right to modify entities, to migrateentities from one non-volatile solid state storage unit to anothernon-volatile solid state storage unit, and to add and remove copies ofentities. This allows for maintaining the redundancy of the underlyingdata. When an authority owner fails, is going to be decommissioned, oris overloaded, the authority is transferred to a new storage node.Transient failures make it non-trivial to ensure that all non-faultymachines agree upon the new authority location. The ambiguity thatarises due to transient failures can be achieved automatically by aconsensus protocol such as Paxos, hot-warm failover schemes, via manualintervention by a remote system administrator, or by a local hardwareadministrator (such as by physically removing the failed machine fromthe cluster, or pressing a button on the failed machine). In someembodiments, a consensus protocol is used, and failover is automatic. Iftoo many failures or replication events occur in too short a timeperiod, the system goes into a self-preservation mode and haltsreplication and data movement activities until an administratorintervenes in accordance with some embodiments.

As authorities are transferred between storage nodes and authorityowners update entities in their authorities, the system transfersmessages between the storage nodes and non-volatile solid state storageunits. With regard to persistent messages, messages that have differentpurposes are of different types. Depending on the type of the message,the system maintains different ordering and durability guarantees. Asthe persistent messages are being processed, the messages aretemporarily stored in multiple durable and non-durable storage hardwaretechnologies. In some embodiments, messages are stored in RAM, NVRAM andon NAND flash devices, and a variety of protocols are used in order tomake efficient use of each storage medium. Latency-sensitive clientrequests may be persisted in replicated NVRAM, and then later NAND,while background rebalancing operations are persisted directly to NAND.

Persistent messages are persistently stored prior to being transmitted.This allows the system to continue to serve client requests despitefailures and component replacement. Although many hardware componentscontain unique identifiers that are visible to system administrators,manufacturer, hardware supply chain and ongoing monitoring qualitycontrol infrastructure, applications running on top of theinfrastructure address virtualize addresses. These virtualized addressesdo not change over the lifetime of the storage system, regardless ofcomponent failures and replacements. This allows each component of thestorage system to be replaced over time without reconfiguration ordisruptions of client request processing, i.e., the system supportsnon-disruptive upgrades.

In some embodiments, the virtualized addresses are stored withsufficient redundancy. A continuous monitoring system correlateshardware and software status and the hardware identifiers. This allowsdetection and prediction of failures due to faulty components andmanufacturing details. The monitoring system also enables the proactivetransfer of authorities and entities away from impacted devices beforefailure occurs by removing the component from the critical path in someembodiments.

FIG. 2C is a multiple level block diagram, showing contents of a storagenode 150 and contents of a non-volatile solid state storage 152 of thestorage node 150. Data is communicated to and from the storage node 150by a network interface controller (‘NIC’) 202 in some embodiments. Eachstorage node 150 has a CPU 156, and one or more non-volatile solid statestorage 152, as discussed above. Moving down one level in FIG. 2C, eachnon-volatile solid state storage 152 has a relatively fast non-volatilesolid state memory, such as nonvolatile random access memory (‘NVRAM’)204, and flash memory 206. In some embodiments, NVRAM 204 may be acomponent that does not require program/erase cycles (DRAM, MRAM, PCM),and can be a memory that can support being written vastly more oftenthan the memory is read from. Moving down another level in FIG. 2C, theNVRAM 204 is implemented in one embodiment as high speed volatilememory, such as dynamic random access memory (DRAM) 216, backed up byenergy reserve 218. Energy reserve 218 provides sufficient electricalpower to keep the DRAM 216 powered long enough for contents to betransferred to the flash memory 206 in the event of power failure. Insome embodiments, energy reserve 218 is a capacitor, super-capacitor,battery, or other device, that supplies a suitable supply of energysufficient to enable the transfer of the contents of DRAM 216 to astable storage medium in the case of power loss. The flash memory 206 isimplemented as multiple flash dies 222, which may be referred to aspackages of flash dies 222 or an array of flash dies 222. It should beappreciated that the flash dies 222 could be packaged in any number ofways, with a single die per package, multiple dies per package (i.e.multichip packages), in hybrid packages, as bare dies on a printedcircuit board or other substrate, as encapsulated dies, etc. In theembodiment shown, the non-volatile solid state storage 152 has acontroller 212 or other processor, and an input output (I/O) port 210coupled to the controller 212. I/O port 210 is coupled to the CPU 156and/or the network interface controller 202 of the flash storage node150. Flash input output (I/O) port 220 is coupled to the flash dies 222,and a direct memory access unit (DMA) 214 is coupled to the controller212, the DRAM 216 and the flash dies 222. In the embodiment shown, theI/O port 210, controller 212, DMA unit 214 and flash I/O port 220 areimplemented on a programmable logic device (‘PLD’) 208, e.g., a fieldprogrammable gate array (FPGA). In this embodiment, each flash die 222has pages, organized as sixteen kB (kilobyte) pages 224, and a register226 through which data can be written to or read from the flash die 222.In further embodiments, other types of solid-state memory are used inplace of, or in addition to flash memory illustrated within flash die222.

Storage clusters 161, in various embodiments as disclosed herein, can becontrasted with storage arrays in general. The storage nodes 150 arepart of a collection that creates the storage cluster 161. Each storagenode 150 owns a slice of data and computing required to provide thedata. Multiple storage nodes 150 cooperate to store and retrieve thedata. Storage memory or storage devices, as used in storage arrays ingeneral, are less involved with processing and manipulating the data.Storage memory or storage devices in a storage array receive commands toread, write, or erase data. The storage memory or storage devices in astorage array are not aware of a larger system in which they areembedded, or what the data means. Storage memory or storage devices instorage arrays can include various types of storage memory, such as RAM,solid state drives, hard disk drives, etc. The storage units 152described herein have multiple interfaces active simultaneously andserving multiple purposes. In some embodiments, some of thefunctionality of a storage node 150 is shifted into a storage unit 152,transforming the storage unit 152 into a combination of storage unit 152and storage node 150. Placing computing (relative to storage data) intothe storage unit 152 places this computing closer to the data itself.The various system embodiments have a hierarchy of storage node layerswith different capabilities. By contrast, in a storage array, acontroller owns and knows everything about all of the data that thecontroller manages in a shelf or storage devices. In a storage cluster161, as described herein, multiple controllers in multiple storage units152 and/or storage nodes 150 cooperate in various ways (e.g., forerasure coding, data sharding, metadata communication and redundancy,storage capacity expansion or contraction, data recovery, and so on).

FIG. 2D shows a storage server environment, which uses embodiments ofthe storage nodes 150 and storage units 152 of FIGS. 2A-C. In thisversion, each storage unit 152 has a processor such as controller 212(see FIG. 2C), an FPGA (field programmable gate array), flash memory206, and NVRAM 204 (which is super-capacitor backed DRAM 216, see FIGS.2B and 2C) on a PCIe (peripheral component interconnect express) boardin a chassis 138 (see FIG. 2A). The storage unit 152 may be implementedas a single board containing storage, and may be the largest tolerablefailure domain inside the chassis. In some embodiments, up to twostorage units 152 may fail and the device will continue with no dataloss.

The physical storage is divided into named regions based on applicationusage in some embodiments. The NVRAM 204 is a contiguous block ofreserved memory in the storage unit 152 DRAM 216, and is backed by NANDflash. NVRAM 204 is logically divided into multiple memory regionswritten for two as spool (e.g., spool_region). Space within the NVRAM204 spools is managed by each authority 168 independently. Each deviceprovides an amount of storage space to each authority 168. Thatauthority 168 further manages lifetimes and allocations within thatspace. Examples of a spool include distributed transactions or notions.When the primary power to a storage unit 152 fails, onboardsuper-capacitors provide a short duration of power hold up. During thisholdup interval, the contents of the NVRAM 204 are flushed to flashmemory 206. On the next power-on, the contents of the NVRAM 204 arerecovered from the flash memory 206.

As for the storage unit controller, the responsibility of the logical“controller” is distributed across each of the blades containingauthorities 168. This distribution of logical control is shown in FIG.2D as a host controller 242, mid-tier controller 244 and storage unitcontroller(s) 246. Management of the control plane and the storage planeare treated independently, although parts may be physically co-locatedon the same blade. Each authority 168 effectively serves as anindependent controller. Each authority 168 provides its own data andmetadata structures, its own background workers, and maintains its ownlifecycle.

FIG. 2E is a blade 252 hardware block diagram, showing a control plane254, compute and storage planes 256, 258, and authorities 168interacting with underlying physical resources, using embodiments of thestorage nodes 150 and storage units 152 of FIGS. 2A-C in the storageserver environment of FIG. 2D. The control plane 254 is partitioned intoa number of authorities 168 which can use the compute resources in thecompute plane 256 to run on any of the blades 252. The storage plane 258is partitioned into a set of devices, each of which provides access toflash 206 and NVRAM 204 resources.

In the compute and storage planes 256, 258 of FIG. 2E, the authorities168 interact with the underlying physical resources (i.e., devices).From the point of view of an authority 168, its resources are stripedover all of the physical devices. From the point of view of a device, itprovides resources to all authorities 168, irrespective of where theauthorities happen to run. Each authority 168 has allocated or has beenallocated one or more partitions 260 of storage memory in the storageunits 152, e.g. partitions 260 in flash memory 206 and NVRAM 204. Eachauthority 168 uses those allocated partitions 260 that belong to it, forwriting or reading user data. Authorities can be associated withdiffering amounts of physical storage of the system. For example, oneauthority 168 could have a larger number of partitions 260 or largersized partitions 260 in one or more storage units 152 than one or moreother authorities 168.

FIG. 2F depicts elasticity software layers in blades 252 of a storagecluster, in accordance with some embodiments. In the elasticitystructure, elasticity software is symmetric, i.e., each blade's computemodule 270 runs the three identical layers of processes depicted in FIG.2F. Storage managers 274 execute read and write requests from otherblades 252 for data and metadata stored in local storage unit 152 NVRAM204 and flash 206. Authorities 168 fulfill client requests by issuingthe necessary reads and writes to the blades 252 on whose storage units152 the corresponding data or metadata resides. Endpoints 272 parseclient connection requests received from switch fabric 146 supervisorysoftware, relay the client connection requests to the authorities 168responsible for fulfillment, and relay the authorities' 168 responses toclients. The symmetric three-layer structure enables the storagesystem's high degree of concurrency. Elasticity scales out efficientlyand reliably in these embodiments. In addition, elasticity implements aunique scale-out technique that balances work evenly across allresources regardless of client access pattern, and maximizes concurrencyby eliminating much of the need for inter-blade coordination thattypically occurs with conventional distributed locking.

Still referring to FIG. 2F, authorities 168 running in the computemodules 270 of a blade 252 perform the internal operations required tofulfill client requests. One feature of elasticity is that authorities168 are stateless, i.e., they cache active data and metadata in theirown blades' 252 DRAMs for fast access, but the authorities store everyupdate in their NVRAM 204 partitions on three separate blades 252 untilthe update has been written to flash 206. All the storage system writesto NVRAM 204 are in triplicate to partitions on three separate blades252 in some embodiments. With triple-mirrored NVRAM 204 and persistentstorage protected by parity and Reed-Solomon RAID checksums, the storagesystem can survive concurrent failure of two blades 252 with no loss ofdata, metadata, or access to either.

Because authorities 168 are stateless, they can migrate between blades252. Each authority 168 has a unique identifier. NVRAM 204 and flash 206partitions are associated with authorities' 168 identifiers, not withthe blades 252 on which they are running in some. Thus, when anauthority 168 migrates, the authority 168 continues to manage the samestorage partitions from its new location. When a new blade 252 isinstalled in an embodiment of the storage cluster, the systemautomatically rebalances load by: partitioning the new blade's 252storage for use by the system's authorities 168, migrating selectedauthorities 168 to the new blade 252, starting endpoints 272 on the newblade 252 and including them in the switch fabric's 146 clientconnection distribution algorithm.

From their new locations, migrated authorities 168 persist the contentsof their NVRAM 204 partitions on flash 206, process read and writerequests from other authorities 168, and fulfill the client requeststhat endpoints 272 direct to them. Similarly, if a blade 252 fails or isremoved, the system redistributes its authorities 168 among the system'sremaining blades 252. The redistributed authorities 168 continue toperform their original functions from their new locations.

FIG. 2G depicts authorities 168 and storage resources in blades 252 of astorage cluster, in accordance with some embodiments. Each authority 168is exclusively responsible for a partition of the flash 206 and NVRAM204 on each blade 252. The authority 168 manages the content andintegrity of its partitions independently of other authorities 168.Authorities 168 compress incoming data and preserve it temporarily intheir NVRAM 204 partitions, and then consolidate, RAID-protect, andpersist the data in segments of the storage in their flash 206partitions. As the authorities 168 write data to flash 206, storagemanagers 274 perform the necessary flash translation to optimize writeperformance and maximize media longevity. In the background, authorities168 “garbage collect,” or reclaim space occupied by data that clientshave made obsolete by overwriting the data. It should be appreciatedthat since authorities' 168 partitions are disjoint, there is no needfor distributed locking to execute client and writes or to performbackground functions.

The embodiments described herein may utilize various software,communication and/or networking protocols. In addition, theconfiguration of the hardware and/or software may be adjusted toaccommodate various protocols. For example, the embodiments may utilizeActive Directory, which is a database based system that providesauthentication, directory, policy, and other services in a WINDOWS™environment. In these embodiments, LDAP (Lightweight Directory AccessProtocol) is one example application protocol for querying and modifyingitems in directory service providers such as Active Directory. In someembodiments, a network lock manager (‘NLM’) is utilized as a facilitythat works in cooperation with the Network File System (‘NFS’) toprovide a System V style of advisory file and record locking over anetwork. The Server Message Block (‘SMB’) protocol, one version of whichis also known as Common Internet File System (‘CIFS’), may be integratedwith the storage systems discussed herein. SMP operates as anapplication-layer network protocol typically used for providing sharedaccess to files, printers, and serial ports and miscellaneouscommunications between nodes on a network. SMB also provides anauthenticated inter-process communication mechanism. AMAZON™ S3 (SimpleStorage Service) is a web service offered by Amazon Web Services, andthe systems described herein may interface with Amazon S3 through webservices interfaces (REST (representational state transfer), SOAP(simple object access protocol), and BitTorrent). A RESTful API(application programming interface) breaks down a transaction to createa series of small modules. Each module addresses a particular underlyingpart of the transaction. The control or permissions provided with theseembodiments, especially for object data, may include utilization of anaccess control list (‘ACL’). The ACL is a list of permissions attachedto an object and the ACL specifies which users or system processes aregranted access to objects, as well as what operations are allowed ongiven objects. The systems may utilize Internet Protocol version 6(‘IPv6’), as well as IPv4, for the communications protocol that providesan identification and location system for computers on networks androutes traffic across the Internet. The routing of packets betweennetworked systems may include Equal-cost multi-path routing (‘ECMP’),which is a routing strategy where next-hop packet forwarding to a singledestination can occur over multiple “best paths” which tie for top placein routing metric calculations. Multi-path routing can be used inconjunction with most routing protocols, because it is a per-hopdecision limited to a single router. The software may supportMulti-tenancy, which is an architecture in which a single instance of asoftware application serves multiple customers. Each customer may bereferred to as a tenant. Tenants may be given the ability to customizesome parts of the application, but may not customize the application'scode, in some embodiments. The embodiments may maintain audit logs. Anaudit log is a document that records an event in a computing system. Inaddition to documenting what resources were accessed, audit log entriestypically include destination and source addresses, a timestamp, anduser login information for compliance with various regulations. Theembodiments may support various key management policies, such asencryption key rotation. In addition, the system may support dynamicroot passwords or some variation dynamically changing passwords.

FIG. 3A sets forth a diagram of a storage system 306 that is coupled fordata communications with a cloud services provider 302 in accordancewith some embodiments of the present disclosure. Although depicted inless detail, the storage system 306 depicted in FIG. 3A may be similarto the storage systems described above with reference to FIGS. 1A-1D andFIGS. 2A-2G. In some embodiments, the storage system 306 depicted inFIG. 3A may be embodied as a storage system that includes imbalancedactive/active controllers, as a storage system that includes balancedactive/active controllers, as a storage system that includesactive/active controllers where less than all of each controller'sresources are utilized such that each controller has reserve resourcesthat may be used to support failover, as a storage system that includesfully active/active controllers, as a storage system that includesdataset-segregated controllers, as a storage system that includesdual-layer architectures with front-end controllers and back-endintegrated storage controllers, as a storage system that includesscale-out clusters of dual-controller arrays, as well as combinations ofsuch embodiments.

In the example depicted in FIG. 3A, the storage system 306 is coupled tothe cloud services provider 302 via a data communications link 304. Thedata communications link 304 may be embodied as a dedicated datacommunications link, as a data communications pathway that is providedthrough the use of one or data communications networks such as a widearea network (‘WAN’) or local area network (‘LAN’), or as some othermechanism capable of transporting digital information between thestorage system 306 and the cloud services provider 302. Such a datacommunications link 304 may be fully wired, fully wireless, or someaggregation of wired and wireless data communications pathways. In suchan example, digital information may be exchanged between the storagesystem 306 and the cloud services provider 302 via the datacommunications link 304 using one or more data communications protocols.For example, digital information may be exchanged between the storagesystem 306 and the cloud services provider 302 via the datacommunications link 304 using the handheld device transfer protocol(‘HDTP’), hypertext transfer protocol (‘HTTP’), internet protocol(‘IP’), real-time transfer protocol (‘RTP’), transmission controlprotocol (‘TCP’), user datagram protocol (‘UDP’), wireless applicationprotocol (‘WAP’), or other protocol.

The cloud services provider 302 depicted in FIG. 3A may be embodied, forexample, as a system and computing environment that provides services tousers of the cloud services provider 302 through the sharing ofcomputing resources via the data communications link 304. The cloudservices provider 302 may provide on-demand access to a shared pool ofconfigurable computing resources such as computer networks, servers,storage, applications and services, and so on. The shared pool ofconfigurable resources may be rapidly provisioned and released to a userof the cloud services provider 302 with minimal management effort.Generally, the user of the cloud services provider 302 is unaware of theexact computing resources utilized by the cloud services provider 302 toprovide the services. Although in many cases such a cloud servicesprovider 302 may be accessible via the Internet, readers of skill in theart will recognize that any system that abstracts the use of sharedresources to provide services to a user through any data communicationslink may be considered a cloud services provider 302.

In the example depicted in FIG. 3A, the cloud services provider 302 maybe configured to provide a variety of services to the storage system 306and users of the storage system 306 through the implementation ofvarious service models. For example, the cloud services provider 302 maybe configured to provide services to the storage system 306 and users ofthe storage system 306 through the implementation of an infrastructureas a service (‘IaaS’) service model where the cloud services provider302 offers computing infrastructure such as virtual machines and otherresources as a service to subscribers. In addition, the cloud servicesprovider 302 may be configured to provide services to the storage system306 and users of the storage system 306 through the implementation of aplatform as a service (‘PaaS’) service model where the cloud servicesprovider 302 offers a development environment to application developers.Such a development environment may include, for example, an operatingsystem, programming-language execution environment, database, webserver, or other components that may be utilized by applicationdevelopers to develop and run software solutions on a cloud platform.Furthermore, the cloud services provider 302 may be configured toprovide services to the storage system 306 and users of the storagesystem 306 through the implementation of a software as a service(‘SaaS’) service model where the cloud services provider 302 offersapplication software, databases, as well as the platforms that are usedto run the applications to the storage system 306 and users of thestorage system 306, providing the storage system 306 and users of thestorage system 306 with on-demand software and eliminating the need toinstall and run the application on local computers, which may simplifymaintenance and support of the application. The cloud services provider302 may be further configured to provide services to the storage system306 and users of the storage system 306 through the implementation of anauthentication as a service (‘AaaS’) service model where the cloudservices provider 302 offers authentication services that can be used tosecure access to applications, data sources, or other resources. Thecloud services provider 302 may also be configured to provide servicesto the storage system 306 and users of the storage system 306 throughthe implementation of a storage as a service model where the cloudservices provider 302 offers access to its storage infrastructure foruse by the storage system 306 and users of the storage system 306.Readers will appreciate that the cloud services provider 302 may beconfigured to provide additional services to the storage system 306 andusers of the storage system 306 through the implementation of additionalservice models, as the service models described above are included onlyfor explanatory purposes and in no way represent a limitation of theservices that may be offered by the cloud services provider 302 or alimitation as to the service models that may be implemented by the cloudservices provider 302.

In the example depicted in FIG. 3A, the cloud services provider 302 maybe embodied, for example, as a private cloud, as a public cloud, or as acombination of a private cloud and public cloud. In an embodiment inwhich the cloud services provider 302 is embodied as a private cloud,the cloud services provider 302 may be dedicated to providing servicesto a single organization rather than providing services to multipleorganizations. In an embodiment where the cloud services provider 302 isembodied as a public cloud, the cloud services provider 302 may provideservices to multiple organizations. Public cloud and private clouddeployment models may differ and may come with various advantages anddisadvantages. For example, because a public cloud deployment involvesthe sharing of a computing infrastructure across different organization,such a deployment may not be ideal for organizations with securityconcerns, mission-critical workloads, uptime requirements demands, andso on. While a private cloud deployment can address some of theseissues, a private cloud deployment may require on-premises staff tomanage the private cloud. In still alternative embodiments, the cloudservices provider 302 may be embodied as a mix of a private and publiccloud services with a hybrid cloud deployment.

Although not explicitly depicted in FIG. 3A, readers will appreciatethat additional hardware components and additional software componentsmay be necessary to facilitate the delivery of cloud services to thestorage system 306 and users of the storage system 306. For example, thestorage system 306 may be coupled to (or even include) a cloud storagegateway. Such a cloud storage gateway may be embodied, for example, ashardware-based or software-based appliance that is located on premisewith the storage system 306. Such a cloud storage gateway may operate asa bridge between local applications that are executing on the storagearray 306 and remote, cloud-based storage that is utilized by thestorage array 306. Through the use of a cloud storage gateway,organizations may move primary iSCSI or NAS to the cloud servicesprovider 302, thereby enabling the organization to save space on theiron-premises storage systems. Such a cloud storage gateway may beconfigured to emulate a disk array, a block-based device, a file server,or other storage system that can translate the SCSI commands, fileserver commands, or other appropriate command into REST-space protocolsthat facilitate communications with the cloud services provider 302.

In order to enable the storage system 306 and users of the storagesystem 306 to make use of the services provided by the cloud servicesprovider 302, a cloud migration process may take place during whichdata, applications, or other elements from an organization's localsystems (or even from another cloud environment) are moved to the cloudservices provider 302. In order to successfully migrate data,applications, or other elements to the cloud services provider's 302environment, middleware such as a cloud migration tool may be utilizedto bridge gaps between the cloud services provider's 302 environment andan organization's environment. Such cloud migration tools may also beconfigured to address potentially high network costs and long transfertimes associated with migrating large volumes of data to the cloudservices provider 302, as well as addressing security concernsassociated with sensitive data to the cloud services provider 302 overdata communications networks. In order to further enable the storagesystem 306 and users of the storage system 306 to make use of theservices provided by the cloud services provider 302, a cloudorchestrator may also be used to arrange and coordinate automated tasksin pursuit of creating a consolidated process or workflow. Such a cloudorchestrator may perform tasks such as configuring various components,whether those components are cloud components or on-premises components,as well as managing the interconnections between such components. Thecloud orchestrator can simplify the inter-component communication andconnections to ensure that links are correctly configured andmaintained.

In the example depicted in FIG. 3A, and as described briefly above, thecloud services provider 302 may be configured to provide services to thestorage system 306 and users of the storage system 306 through the usageof a SaaS service model where the cloud services provider 302 offersapplication software, databases, as well as the platforms that are usedto run the applications to the storage system 306 and users of thestorage system 306, providing the storage system 306 and users of thestorage system 306 with on-demand software and eliminating the need toinstall and run the application on local computers, which may simplifymaintenance and support of the application. Such applications may takemany forms in accordance with various embodiments of the presentdisclosure. For example, the cloud services provider 302 may beconfigured to provide access to data analytics applications to thestorage system 306 and users of the storage system 306. Such dataanalytics applications may be configured, for example, to receivetelemetry data phoned home by the storage system 306. Such telemetrydata may describe various operating characteristics of the storagesystem 306 and may be analyzed, for example, to determine the health ofthe storage system 306, to identify workloads that are executing on thestorage system 306, to predict when the storage system 306 will run outof various resources, to recommend configuration changes, hardware orsoftware upgrades, workflow migrations, or other actions that mayimprove the operation of the storage system 306.

The cloud services provider 302 may also be configured to provide accessto virtualized computing environments to the storage system 306 andusers of the storage system 306. Such virtualized computing environmentsmay be embodied, for example, as a virtual machine or other virtualizedcomputer hardware platforms, virtual storage devices, virtualizedcomputer network resources, and so on. Examples of such virtualizedenvironments can include virtual machines that are created to emulate anactual computer, virtualized desktop environments that separate alogical desktop from a physical machine, virtualized file systems thatallow uniform access to different types of concrete file systems, andmany others.

For further explanation, FIG. 3B sets forth a diagram of a storagesystem 306 in accordance with some embodiments of the presentdisclosure. Although depicted in less detail, the storage system 306depicted in FIG. 3B may be similar to the storage systems describedabove with reference to FIGS. 1A-1D and FIGS. 2A-2G as the storagesystem may include many of the components described above.

The storage system 306 depicted in FIG. 3B may include storage resources308, which may be embodied in many forms. For example, in someembodiments the storage resources 308 can include nano-RAM or anotherform of nonvolatile random access memory that utilizes carbon nanotubesdeposited on a substrate. In some embodiments, the storage resources 308may include 3D crosspoint non-volatile memory in which bit storage isbased on a change of bulk resistance, in conjunction with a stackablecross-gridded data access array. In some embodiments, the storageresources 308 may include flash memory, including single-level cell(‘SLC’) NAND flash, multi-level cell (‘MLC’) NAND flash, triple-levelcell (‘TLC’) NAND flash, quad-level cell (‘QLC’) NAND flash, and others.In some embodiments, the storage resources 308 may include non-volatilemagnetoresistive random-access memory (‘MRAM’), including spin transfertorque (‘STT’) MRAM, in which data is stored through the use of magneticstorage elements. In some embodiments, the example storage resources 308may include non-volatile phase-change memory (‘PCM’) that may have theability to hold multiple bits in a single cell as cells can achieve anumber of distinct intermediary states. In some embodiments, the storageresources 308 may include quantum memory that allows for the storage andretrieval of photonic quantum information. In some embodiments, theexample storage resources 308 may include resistive random-access memory(‘ReRAM’) in which data is stored by changing the resistance across adielectric solid-state material. In some embodiments, the storageresources 308 may include storage class memory (‘SCM’) in whichsolid-state nonvolatile memory may be manufactured at a high densityusing some combination of sub-lithographic patterning techniques,multiple bits per cell, multiple layers of devices, and so on. Readerswill appreciate that other forms of computer memories and storagedevices may be utilized by the storage systems described above,including DRAM, SRAM, EEPROM, universal memory, and many others. Thestorage resources 308 depicted in FIG. 3A may be embodied in a varietyof form factors, including but not limited to, dual in-line memorymodules (‘DIMMs’), non-volatile dual in-line memory modules (‘NVDIMMs’),M.2, U.2, and others.

The example storage system 306 depicted in FIG. 3B may implement avariety of storage architectures. For example, storage systems inaccordance with some embodiments of the present disclosure may utilizeblock storage where data is stored in blocks, and each block essentiallyacts as an individual hard drive. Storage systems in accordance withsome embodiments of the present disclosure may utilize object storage,where data is managed as objects. Each object may include the dataitself, a variable amount of metadata, and a globally unique identifier,where object storage can be implemented at multiple levels (e.g., devicelevel, system level, interface level). Storage systems in accordancewith some embodiments of the present disclosure utilize file storage inwhich data is stored in a hierarchical structure. Such data may be savedin files and folders, and presented to both the system storing it andthe system retrieving it in the same format.

The example storage system 306 depicted in FIG. 3B may be embodied as astorage system in which additional storage resources can be addedthrough the use of a scale-up model, additional storage resources can beadded through the use of a scale-out model, or through some combinationthereof. In a scale-up model, additional storage may be added by addingadditional storage devices. In a scale-out model, however, additionalstorage nodes may be added to a cluster of storage nodes, where suchstorage nodes can include additional processing resources, additionalnetworking resources, and so on.

The storage system 306 depicted in FIG. 3B also includes communicationsresources 310 that may be useful in facilitating data communicationsbetween components within the storage system 306, as well as datacommunications between the storage system 306 and computing devices thatare outside of the storage system 306. The communications resources 310may be configured to utilize a variety of different protocols and datacommunication fabrics to facilitate data communications betweencomponents within the storage systems as well as computing devices thatare outside of the storage system. For example, the communicationsresources 310 can include fibre channel (‘FC’) technologies such as FCfabrics and FC protocols that can transport SCSI commands over FCnetworks. The communications resources 310 can also include FC overethernet (‘FCoE’) technologies through which FC frames are encapsulatedand transmitted over Ethernet networks. The communications resources 310can also include InfiniBand (‘IB’) technologies in which a switchedfabric topology is utilized to facilitate transmissions between channeladapters. The communications resources 310 can also include NVM Express(‘NVMe’) technologies and NVMe over fabrics (‘NVMeoF’) technologiesthrough which non-volatile storage media attached via a PCI express(‘PCIe’) bus may be accessed. The communications resources 310 can alsoinclude mechanisms for accessing storage resources 308 within thestorage system 306 utilizing serial attached SCSI (‘SAS’), serial ATA(‘SATA’) bus interfaces for connecting storage resources 308 within thestorage system 306 to host bus adapters within the storage system 306,internet small computer systems interface (‘iSCSI’) technologies toprovide block-level access to storage resources 308 within the storagesystem 306, and other communications resources that that may be usefulin facilitating data communications between components within thestorage system 306, as well as data communications between the storagesystem 306 and computing devices that are outside of the storage system306.

The storage system 306 depicted in FIG. 3B also includes processingresources 312 that may be useful in useful in executing computer programinstructions and performing other computational tasks within the storagesystem 306. The processing resources 312 may include one or moreapplication-specific integrated circuits (‘ASICs’) that are customizedfor some particular purpose as well as one or more central processingunits (‘CPUs’). The processing resources 312 may also include one ormore digital signal processors (‘DSPs’), one or more field-programmablegate arrays (‘FPGAs’), one or more systems on a chip (‘SoCs’), or otherform of processing resources 312. The storage system 306 may utilize thestorage resources 312 to perform a variety of tasks including, but notlimited to, supporting the execution of software resources 314 that willbe described in greater detail below.

The storage system 306 depicted in FIG. 3B also includes softwareresources 314 that, when executed by processing resources 312 within thestorage system 306, may perform various tasks. The software resources314 may include, for example, one or more modules of computer programinstructions that when executed by processing resources 312 within thestorage system 306 are useful in carrying out various data protectiontechniques to preserve the integrity of data that is stored within thestorage systems. Readers will appreciate that such data protectiontechniques may be carried out, for example, by system software executingon computer hardware within the storage system, by a cloud servicesprovider, or in other ways. Such data protection techniques can include,for example, data archiving techniques that cause data that is no longeractively used to be moved to a separate storage device or separatestorage system for long-term retention, data backup techniques throughwhich data stored in the storage system may be copied and stored in adistinct location to avoid data loss in the event of equipment failureor some other form of catastrophe with the storage system, datareplication techniques through which data stored in the storage systemis replicated to another storage system such that the data may beaccessible via multiple storage systems, data snapshotting techniquesthrough which the state of data within the storage system is captured atvarious points in time, data and database cloning techniques throughwhich duplicate copies of data and databases may be created, and otherdata protection techniques. Through the use of such data protectiontechniques, business continuity and disaster recovery objectives may bemet as a failure of the storage system may not result in the loss ofdata stored in the storage system.

The software resources 314 may also include software that is useful inimplementing software-defined storage (‘SDS’). In such an example, thesoftware resources 314 may include one or more modules of computerprogram instructions that, when executed, are useful in policy-basedprovisioning and management of data storage that is independent of theunderlying hardware. Such software resources 314 may be useful inimplementing storage virtualization to separate the storage hardwarefrom the software that manages the storage hardware.

The software resources 314 may also include software that is useful infacilitating and optimizing I/O operations that are directed to thestorage resources 308 in the storage system 306. For example, thesoftware resources 314 may include software modules that perform carryout various data reduction techniques such as, for example, datacompression, data deduplication, and others. The software resources 314may include software modules that intelligently group together I/Ooperations to facilitate better usage of the underlying storage resource308, software modules that perform data migration operations to migratefrom within a storage system, as well as software modules that performother functions. Such software resources 314 may be embodied as one ormore software containers or in many other ways.

Readers will appreciate that the various components depicted in FIG. 3Bmay be grouped into one or more optimized computing packages asconverged infrastructures. Such converged infrastructures may includepools of computers, storage and networking resources that can be sharedby multiple applications and managed in a collective manner usingpolicy-driven processes. Such converged infrastructures may minimizecompatibility issues between various components within the storagesystem 306 while also reducing various costs associated with theestablishment and operation of the storage system 306. Such convergedinfrastructures may be implemented with a converged infrastructurereference architecture, with standalone appliances, with a softwaredriven hyper-converged approach (e.g., hyper-converged infrastructures),or in other ways.

Readers will appreciate that the storage system 306 depicted in FIG. 3Bmay be useful for supporting various types of software applications. Forexample, the storage system 306 may be useful in supporting artificialintelligence (‘AI’) applications, database applications, DevOpsprojects, electronic design automation tools, event-driven softwareapplications, high performance computing applications, simulationapplications, high-speed data capture and analysis applications, machinelearning applications, media production applications, media servingapplications, picture archiving and communication systems (‘PACS’)applications, software development applications, virtual realityapplications, augmented reality applications, and many other types ofapplications by providing storage resources to such applications.

The storage systems described above may operate to support a widevariety of applications. In view of the fact that the storage systemsinclude compute resources, storage resources, and a wide variety ofother resources, the storage systems may be well suited to supportapplications that are resource intensive such as, for example, AIapplications. Such AI applications may enable devices to perceive theirenvironment and take actions that maximize their chance of success atsome goal. Examples of such AI applications can include IBM Watson,Microsoft Oxford, Google DeepMind, Baidu Minwa, and others. The storagesystems described above may also be well suited to support other typesof applications that are resource intensive such as, for example,machine learning applications. Machine learning applications may performvarious types of data analysis to automate analytical model building.Using algorithms that iteratively learn from data, machine learningapplications can enable computers to learn without being explicitlyprogrammed.

In addition to the resources already described, the storage systemsdescribed above may also include graphics processing units (‘GPUs’),occasionally referred to as visual processing unit (‘VPUs’). Such GPUsmay be embodied as specialized electronic circuits that rapidlymanipulate and alter memory to accelerate the creation of images in aframe buffer intended for output to a display device. Such GPUs may beincluded within any of the computing devices that are part of thestorage systems described above, including as one of many individuallyscalable components of a storage system, where other examples ofindividually scalable components of such storage system can includestorage components, memory components, compute components (e.g., CPUs,FPGAs, ASICs), networking components, software components, and others.In addition to GPUs, the storage systems described above may alsoinclude neural network processors (‘NNPs’) for use in various aspects ofneural network processing. Such NNPs may be used in place of (or inaddition to) GPUs and may be also be independently scalable.

As described above, the storage systems described herein may beconfigured to support artificial intelligence applications, machinelearning applications, big data analytics applications, and many othertypes of applications. The rapid growth in these sort of applications isbeing driven by three technologies: deep learning (DL), GPU processors,and Big Data. Deep learning is a computing model that makes use ofmassively parallel neural networks inspired by the human brain. Insteadof experts handcrafting software, a deep learning model writes its ownsoftware by learning from lots of examples. A GPU is a modern processorwith thousands of cores, well-suited to run algorithms that looselyrepresent the parallel nature of the human brain.

Advances in deep neural networks have ignited a new wave of algorithmsand tools for data scientists to tap into their data with artificialintelligence (AI). With improved algorithms, larger data sets, andvarious frameworks (including open-source software libraries for machinelearning across a range of tasks), data scientists are tackling new usecases like autonomous driving vehicles, natural language processing andunderstanding, computer vision, machine reasoning, strong AI, and manyothers. Applications of such techniques may include: machine andvehicular object detection, identification and avoidance; visualrecognition, classification and tagging; algorithmic financial tradingstrategy performance management; simultaneous localization and mapping;predictive maintenance of high-value machinery; prevention against cybersecurity threats, expertise automation; image recognition andclassification; question answering; robotics; text analytics(extraction, classification) and text generation and translation; andmany others. Applications of AI techniques has materialized in a widearray of products include, for example, Amazon Echo's speech recognitiontechnology that allows users to talk to their machines, GoogleTranslate™ which allows for machine-based language translation,Spotify's Discover Weekly that provides recommendations on new songs andartists that a user may like based on the user's usage and trafficanalysis, Quill's text generation offering that takes structured dataand turns it into narrative stories, Chatbots that provide real-time,contextually specific answers to questions in a dialog format, and manyothers. Furthermore, AI may impact a wide variety of industries andsectors. For example, AI solutions may be used in healthcare to takeclinical notes, patient files, research data, and other inputs togenerate potential treatment options for doctors to explore. Likewise,AI solutions may be used by retailers to personalize consumerrecommendations based on a person's digital footprint of behaviors,profile data, or other data.

Data is the heart of modern AI and deep learning algorithms. Beforetraining can begin, one problem that must be addressed revolves aroundcollecting the labeled data that is crucial for training an accurate AImodel. A full scale AI deployment may be required to continuouslycollect, clean, transform, label, and store large amounts of data.Adding additional high quality data points directly translates to moreaccurate models and better insights. Data samples may undergo a seriesof processing steps including, but not limited to: 1) ingesting the datafrom an external source into the training system and storing the data inraw form, 2) cleaning and transforming the data in a format convenientfor training, including linking data samples to the appropriate label,3) exploring parameters and models, quickly testing with a smallerdataset, and iterating to converge on the most promising models to pushinto the production cluster, 4) executing training phases to selectrandom batches of input data, including both new and older samples, andfeeding those into production GPU servers for computation to updatemodel parameters, and 5) evaluating including using a holdback portionof the data not used in training in order to evaluate model accuracy onthe holdout data. This lifecycle may apply for any type of parallelizedmachine learning, not just neural networks or deep learning. Forexample, standard machine learning frameworks may rely on CPUs insteadof GPUs but the data ingest and training workflows may be the same.Readers will appreciate that a single shared storage data hub creates acoordination point throughout the lifecycle without the need for extradata copies among the ingest, preprocessing, and training stages. Rarelyis the ingested data used for only one purpose, and shared storage givesthe flexibility to train multiple different models or apply traditionalanalytics to the data.

Readers will appreciate that each stage in the AI data pipeline may havevarying requirements from the data hub (e.g., the storage system orcollection of storage systems). Scale-out storage systems must deliveruncompromising performance for all manner of access types andpatterns—from small, metadata-heavy to large files, from random tosequential access patterns, and from low to high concurrency. Thestorage systems described above may serve as an ideal AI data hub as thesystems may service unstructured workloads. In the first stage, data isideally ingested and stored on to the same data hub that followingstages will use, in order to avoid excess data copying. The next twosteps can be done on a standard compute server that optionally includesa GPU, and then in the fourth and last stage, full training productionjobs are run on powerful GPU-accelerated servers. Often, there is aproduction pipeline alongside an experimental pipeline operating on thesame dataset. Further, the GPU-accelerated servers can be usedindependently for different models or joined together to train on onelarger model, even spanning multiple systems for distributed training.If the shared storage tier is slow, then data must be copied to localstorage for each phase, resulting in wasted time staging data ontodifferent servers. The ideal data hub for the AI training pipelinedelivers performance similar to data stored locally on the server nodewhile also having the simplicity and performance to enable all pipelinestages to operate concurrently.

A data scientist works to improve the usefulness of the trained modelthrough a wide variety of approaches: more data, better data, smartertraining, and deeper models. In many cases, there will be teams of datascientists sharing the same datasets and working in parallel to producenew and improved training models. Often, there is a team of datascientists working within these phases concurrently on the same shareddatasets. Multiple, concurrent workloads of data processing,experimentation, and full-scale training layer the demands of multipleaccess patterns on the storage tier. In other words, storage cannot justsatisfy large file reads, but must contend with a mix of large and smallfile reads and writes. Finally, with multiple data scientists exploringdatasets and models, it may be critical to store data in its nativeformat to provide flexibility for each user to transform, clean, and usethe data in a unique way. The storage systems described above mayprovide a natural shared storage home for the dataset, with dataprotection redundancy (e.g., by using RAID6) and the performancenecessary to be a common access point for multiple developers andmultiple experiments. Using the storage systems described above mayavoid the need to carefully copy subsets of the data for local work,saving both engineering and GPU-accelerated servers use time. Thesecopies become a constant and growing tax as the raw data set and desiredtransformations constantly update and change.

Readers will appreciate that a fundamental reason why deep learning hasseen a surge in success is the continued improvement of models withlarger data set sizes. In contrast, classical machine learningalgorithms, like logistic regression, stop improving in accuracy atsmaller data set sizes. As such, the separation of compute resources andstorage resources may also allow independent scaling of each tier,avoiding many of the complexities inherent in managing both together. Asthe data set size grows or new data sets are considered, a scale outstorage system must be able to expand easily. Similarly, if moreconcurrent training is required, additional GPUs or other computeresources can be added without concern for their internal storage.Furthermore, the storage systems described above may make building,operating, and growing an AI system easier due to the random readbandwidth provided by the storage systems, the ability to of the storagesystems to randomly read small files (50 KB) high rates (meaning that noextra effort is required to aggregate individual data points to makelarger, storage-friendly files), the ability of the storage systems toscale capacity and performance as either the dataset grows or thethroughput requirements grow, the ability of the storage systems tosupport files or objects, the ability of the storage systems to tuneperformance for large or small files (i.e., no need for the user toprovision filesystems), the ability of the storage systems to supportnon-disruptive upgrades of hardware and software even during productionmodel training, and for many other reasons.

Small file performance of the storage tier may be critical as many typesof inputs, including text, audio, or images will be natively stored assmall files. If the storage tier does not handle small files well, anextra step will be required to pre-process and group samples into largerfiles. Storage, built on top of spinning disks, that relies on SSD as acaching tier, may fall short of the performance needed. Because trainingwith random input batches results in more accurate models, the entiredata set must be accessible with full performance. SSD caches onlyprovide high performance for a small subset of the data and will beineffective at hiding the latency of spinning drives.

Although the preceding paragraphs discuss deep learning applications,readers will appreciate that the storage systems described herein mayalso be part of a distributed deep learning (‘DDL’) platform to supportthe execution of DDL algorithms. Distributed deep learning may can beused to significantly accelerate deep learning with distributedcomputing on GPUs (or other form of accelerator or computer programinstruction executor), such that parallelism can be achieved. Inaddition, the output of training machine learning and deep learningmodels, such as a fully trained machine learning model, may be used fora variety of purposes and in conjunction with other tools. For example,trained machine learning models may be used in conjunction with toolslike Core ML to integrate a broad variety of machine learning modeltypes into an application. In fact, trained models may be run throughCore ML converter tools and inserted into a custom application that canbe deployed on compatible devices. The storage systems described abovemay also be paired with other technologies such as TensorFlow, anopen-source software library for dataflow programming across a range oftasks that may be used for machine learning applications such as neuralnetworks, to facilitate the development of such machine learning models,applications, and so on.

The storage systems described above may also be used in a neuromorphiccomputing environment. Neuromorphic computing is a form of computingthat mimics brain cells. To support neuromorphic computing, anarchitecture of interconnected “neurons” replace traditional computingmodels with low-powered signals that go directly between neurons formore efficient computation. Neuromorphic computing may make use ofvery-large-scale integration (VLSI) systems containing electronic analogcircuits to mimic neuro-biological architectures present in the nervoussystem, as well as analog, digital, mixed-mode analog/digital VLSI, andsoftware systems that implement models of neural systems for perception,motor control, or multisensory integration.

Readers will appreciate that the storage systems described above may beconfigured to support the storage of (among of types of data)blockchains. Such blockchains may be embodied as a continuously growinglist of records, called blocks, which are linked and secured usingcryptography. Each block in a blockchain may contain a hash pointer as alink to a previous block, a timestamp, transaction data, and so on.Blockchains may be designed to be resistant to modification of the dataand can serve as an open, distributed ledger that can recordtransactions between two parties efficiently and in a verifiable andpermanent way. This makes blockchains potentially suitable for therecording of events, medical records, and other records managementactivities, such as identity management, transaction processing, andothers. In addition to supporting the storage and use of blockchaintechnologies, the storage systems described above may also support thestorage and use of derivative items such as, for example, open sourceblockchains and related tools that are part of the IBM™ Hyperledgerproject, permissioned blockchains in which a certain number of trustedparties are allowed to access the block chain, blockchain products thatenable developers to build their own distributed ledger projects, andothers. Readers will appreciate that blockchain technologies may impacta wide variety of industries and sectors. For example, blockchaintechnologies may be used in real estate transactions as blockchain basedcontracts whose use can eliminate the need for 3rd parties and enableself-executing actions when conditions are met. Likewise, universalhealth records can be created by aggregating and placing a person'shealth history onto a blockchain ledger for any healthcare provider, orpermissioned health care providers, to access and update.

Readers will further appreciate that in some embodiments, the storagesystems described above may be paired with other resources to supportthe applications described above. For example, one infrastructure couldinclude primary compute in the form of servers and workstations whichspecialize in using General-purpose computing on graphics processingunits (‘GPGPU’) to accelerate deep learning applications that areinterconnected into a computation engine to train parameters for deepneural networks. Each system may have Ethernet external connectivity,InfiniBand external connectivity, some other form of externalconnectivity, or some combination thereof. In such an example, the GPUscan be grouped for a single large training or used independently totrain multiple models. The infrastructure could also include a storagesystem such as those described above to provide, for example, ascale-out all-flash file or object store through which data can beaccessed via high-performance protocols such as NFS, S3, and so on. Theinfrastructure can also include, for example, redundant top-of-rackEthernet switches connected to storage and compute via ports in MLAGport channels for redundancy. The infrastructure could also includeadditional compute in the form of whitebox servers, optionally withGPUs, for data ingestion, pre-processing, and model debugging. Readerswill appreciate that additional infrastructures are also be possible.

Readers will appreciate that the systems described above may be bettersuited for the applications described above relative to other systemsthat may include, for example, a distributed direct-attached storage(DDAS) solution deployed in server nodes. Such DDAS solutions may bebuilt for handling large, less sequential accesses but may be less ableto handle small, random accesses. Readers will further appreciate thatthe storage systems described above may be utilized to provide aplatform for the applications described above that is preferable to theutilization of cloud-based resources as the storage systems may beincluded in an on-site or in-house infrastructure that is more secure,more locally and internally managed, more robust in feature sets andperformance, or otherwise preferable to the utilization of cloud-basedresources as part of a platform to support the applications describedabove. For example, services built on platforms such as IBM's Watson mayrequire a business enterprise to distribute individual user information,such as financial transaction information or identifiable patientrecords, to other institutions. As such, cloud-based offerings of AI asa service may be less desirable than internally managed and offered AIas a service that is supported by storage systems such as the storagesystems described above, for a wide array of technical reasons as wellas for various business reasons.

Readers will appreciate that the storage systems described above, eitheralone or in coordination with other computing machinery may beconfigured to support other AI related tools. For example, the storagesystems may make use of tools like ONXX or other open neural networkexchange formats that make it easier to transfer models written indifferent AI frameworks. Likewise, the storage systems may be configuredto support tools like Amazon's Gluon that allow developers to prototype,build, and train deep learning models. In fact, the storage systemsdescribed above may be part of a larger platform, such as IBM™ CloudPrivate for Data, that includes integrated data science, dataengineering and application building services. Such platforms mayseamlessly collect, organize, secure, and analyze data across anenterprise, as well as simplify hybrid data management, unified datagovernance and integration, data science and business analytics with asingle solution.

Readers will further appreciate that the storage systems described abovemay also be deployed as an edge solution. Such an edge solution may bein place to optimize cloud computing systems by performing dataprocessing at the edge of the network, near the source of the data. Edgecomputing can push applications, data and computing power (i.e.,services) away from centralized points to the logical extremes of anetwork. Through the use of edge solutions such as the storage systemsdescribed above, computational tasks may be performed using the computeresources provided by such storage systems, data may be storage usingthe storage resources of the storage system, and cloud-based servicesmay be accessed through the use of various resources of the storagesystem (including networking resources). By performing computationaltasks on the edge solution, storing data on the edge solution, andgenerally making use of the edge solution, the consumption of expensivecloud-based resources may be avoided and, in fact, performanceimprovements may be experienced relative to a heavier reliance oncloud-based resources.

While many tasks may benefit from the utilization of an edge solution,some particular uses may be especially suited for deployment in such anenvironment. For example, devices like drones, autonomous cars, robots,and others may require extremely rapid processing—so fast, in fact, thatsending data up to a cloud environment and back to receive dataprocessing support may simply be too slow. Likewise, machines likelocomotives and gas turbines that generate large amounts of informationthrough the use of a wide array of data-generating sensors may benefitfrom the rapid data processing capabilities of an edge solution. As anadditional example, some IoT devices such as connected video cameras maynot be well-suited for the utilization of cloud-based resources as itmay be impractical (not only from a privacy perspective, securityperspective, or a financial perspective) to send the data to the cloudsimply because of the pure volume of data that is involved. As such,many tasks that really on data processing, storage, or communicationsmay be better suited by platforms that include edge solutions such asthe storage systems described above.

Consider a specific example of inventory management in a warehouse,distribution center, or similar location. A large inventory,warehousing, shipping, order-fulfillment, manufacturing or otheroperation has a large amount of inventory on inventory shelves, and highresolution digital cameras that produce a firehose of large data. All ofthis data may be taken into an image processing system, which may reducethe amount of data to a firehose of small data. All of the small datamay be stored on-premises in storage. The on-premises storage, at theedge of the facility, may be coupled to the cloud, for external reports,real-time control and cloud storage. Inventory management may beperformed with the results of the image processing, so that inventorycan be tracked on the shelves and restocked, moved, shipped, modifiedwith new products, or discontinued/obsolescent products deleted, etc.The above scenario is a prime candidate for an embodiment of theconfigurable processing and storage systems described above. Acombination of compute-only blades and offload blades suited for theimage processing, perhaps with deep learning on offload-FPGA oroffload-custom blade(s) could take in the firehose of large data fromall of the digital cameras, and produce the firehose of small data. Allof the small data could then be stored by storage nodes, operating withstorage units in whichever combination of types of storage blades besthandles the data flow. This is an example of storage and functionacceleration and integration. Depending on external communication needswith the cloud, and external processing in the cloud, and depending onreliability of network connections and cloud resources, the system couldbe sized for storage and compute management with bursty workloads andvariable conductivity reliability. Also, depending on other inventorymanagement aspects, the system could be configured for scheduling andresource management in a hybrid edge/cloud environment.

The storage systems described above may alone, or in combination withother computing resources, serves as a network edge platform thatcombines compute resources, storage resources, networking resources,cloud technologies and network virtualization technologies, and so on.As part of the network, the edge may take on characteristics similar toother network facilities, from the customer premise and backhaulaggregation facilities to Points of Presence (PoPs) and regional datacenters. Readers will appreciate that network workloads, such as VirtualNetwork Functions (VNFs) and others, will reside on the network edgeplatform. Enabled by a combination of containers and virtual machines,the network edge platform may rely on controllers and schedulers thatare no longer geographically co-located with the data processingresources. The functions, as microservices, may split into controlplanes, user and data planes, or even state machines, allowing forindependent optimization and scaling techniques to be applied. Such userand data planes may be enabled through increased accelerators, boththose residing in server platforms, such as FPGAs and Smart NICs, andthrough SDN-enabled merchant silicon and programmable ASICs.

The storage systems described above may also be optimized for use in bigdata analytics. Big data analytics may be generally described as theprocess of examining large and varied data sets to uncover hiddenpatterns, unknown correlations, market trends, customer preferences andother useful information that can help organizations make more-informedbusiness decisions. Big data analytics applications enable datascientists, predictive modelers, statisticians and other analyticsprofessionals to analyze growing volumes of structured transaction data,plus other forms of data that are often left untapped by conventionalbusiness intelligence (BI) and analytics programs. As part of thatprocess, semi-structured and unstructured data such as, for example,internet clickstream data, web server logs, social media content, textfrom customer emails and survey responses, mobile-phone call-detailrecords, IoT sensor data, and other data may be converted to astructured form. Big data analytics is a form of advanced analytics,which involves complex applications with elements such as predictivemodels, statistical algorithms and what-if analyses powered byhigh-performance analytics systems.

The storage systems described above may also support (includingimplementing as a system interface) applications that perform tasks inresponse to human speech. For example, the storage systems may supportthe execution intelligent personal assistant applications such as, forexample, Amazon's Alexa, Apple Siri, Google Voice, Samsung Bixby,Microsoft Cortana, and others. While the examples described in theprevious sentence make use of voice as input, the storage systemsdescribed above may also support chatbots, talkbots, chatterbots, orartificial conversational entities or other applications that areconfigured to conduct a conversation via auditory or textual methods.Likewise, the storage system may actually execute such an application toenable a user such as a system administrator to interact with thestorage system via speech. Such applications are generally capable ofvoice interaction, music playback, making to-do lists, setting alarms,streaming podcasts, playing audiobooks, and providing weather, traffic,and other real time information, such as news, although in embodimentsin accordance with the present disclosure, such applications may beutilized as interfaces to various system management operations.

The storage systems described above may also implement AI platforms fordelivering on the vision of self-driving storage. Such AI platforms maybe configured to deliver global predictive intelligence by collectingand analyzing large amounts of storage system telemetry data points toenable effortless management, analytics and support. In fact, suchstorage systems may be capable of predicting both capacity andperformance, as well as generating intelligent advice on workloaddeployment, interaction and optimization. Such AI platforms may beconfigured to scan all incoming storage system telemetry data against alibrary of issue fingerprints to predict and resolve incidents inreal-time, before they impact customer environments, and captureshundreds of variables related to performance that are used to forecastperformance load.

The storage systems described above may support the serialized orsimultaneous execution artificial intelligence applications, machinelearning applications, data analytics applications, datatransformations, and other tasks that collectively may form an AIladder. Such an AI ladder may effectively be formed by combining suchelements to form a complete data science pipeline, where existdependencies between elements of the AI ladder. For example, AI mayrequire that some form of machine learning has taken place, machinelearning may require that some form of analytics has taken place,analytics may require that some form of data and informationarchitecting has taken place, and so on. As such, each element may beviewed as a rung in an AI ladder that collectively can form a completeand sophisticated AI solution.

The storage systems described above may also, either alone or incombination with other computing environments, be used to deliver an AIeverywhere experience where AI permeates wide and expansive aspects ofbusiness and life. For example, AI may play an important role in thedelivery of deep learning solutions, deep reinforcement learningsolutions, artificial general intelligence solutions, autonomousvehicles, cognitive computing solutions, commercial UAVs or drones,conversational user interfaces, enterprise taxonomies, ontologymanagement solutions, machine learning solutions, smart dust, smartrobots, smart workplaces, and many others. The storage systems describedabove may also, either alone or in combination with other computingenvironments, be used to deliver a wide range of transparently immersiveexperiences where technology can introduce transparency between people,businesses, and things. Such transparently immersive experiences may bedelivered as augmented reality technologies, connected homes, virtualreality technologies, brain—computer interfaces, human augmentationtechnologies, nanotube electronics, volumetric displays, 4D printingtechnologies, or others. The storage systems described above may also,either alone or in combination with other computing environments, beused to support a wide variety of digital platforms. Such digitalplatforms can include, for example, 5G wireless systems and platforms,digital twin platforms, edge computing platforms, IoT platforms, quantumcomputing platforms, serverless PaaS, software-defined security,neuromorphic computing platforms, and so on.

The storage systems described above may also be part of a multi-cloudenvironment in which multiple cloud computing and storage services aredeployed in a single heterogeneous architecture. In order to facilitatethe operation of such a multi-cloud environment, DevOps tools may bedeployed to enable orchestration across clouds. Likewise, continuousdevelopment and continuous integration tools may be deployed tostandardize processes around continuous integration and delivery, newfeature rollout and provisioning cloud workloads. By standardizing theseprocesses, a multi-cloud strategy may be implemented that enables theutilization of the best provider for each workload. Furthermore,application monitoring and visibility tools may be deployed to moveapplication workloads around different clouds, identify performanceissues, and perform other tasks. In addition, security and compliancetools may be deployed for to ensure compliance with securityrequirements, government regulations, and so on. Such a multi-cloudenvironment may also include tools for application delivery and smartworkload management to ensure efficient application delivery and helpdirect workloads across the distributed and heterogeneousinfrastructure, as well as tools that ease the deployment andmaintenance of packaged and custom applications in the cloud and enableportability amongst clouds. The multi-cloud environment may similarlyinclude tools for data portability.

The storage systems described above may be used as a part of a platformto enable the use of crypto-anchors that may be used to authenticate aproduct's origins and contents to ensure that it matches a blockchainrecord associated with the product. Such crypto-anchors may take manyforms including, for example, as edible ink, as a mobile sensor, as amicrochip, and others. Similarly, as part of a suite of tools to securedata stored on the storage system, the storage systems described abovemay implement various encryption technologies and schemes, includinglattice cryptography. Lattice cryptography can involve constructions ofcryptographic primitives that involve lattices, either in theconstruction itself or in the security proof. Unlike public-key schemessuch as the RSA, Diffie-Hellman or Elliptic-Curve cryptosystems, whichare easily attacked by a quantum computer, some lattice-basedconstructions appear to be resistant to attack by both classical andquantum computers.

A quantum computer is a device that performs quantum computing. Quantumcomputing is computing using quantum-mechanical phenomena, such assuperposition and entanglement. Quantum computers differ fromtraditional computers that are based on transistors, as such traditionalcomputers require that data be encoded into binary digits (bits), eachof which is always in one of two definite states (0 or 1). In contrastto traditional computers, quantum computers use quantum bits, which canbe in superpositions of states. A quantum computer maintains a sequenceof qubits, where a single qubit can represent a one, a zero, or anyquantum superposition of those two qubit states. A pair of qubits can bein any quantum superposition of 4 states, and three qubits in anysuperposition of 8 states. A quantum computer with n qubits cangenerally be in an arbitrary superposition of up to 2{circumflex over( )}n different states simultaneously, whereas a traditional computercan only be in one of these states at any one time. A quantum Turingmachine is a theoretical model of such a computer.

For further explanation, FIG. 4 sets forth a flow chart illustrating anexample method for executing a big data analytics pipeline in a storagesystem that includes compute resources and shared storage resourcesaccording to some embodiments of the present disclosure. Althoughdepicted in less detail, the storage system (406) depicted in FIG. 4 maybe similar to the storage systems described above with reference toFIGS. 1A-1D, FIGS. 2A-2G, FIGS. 3A-3B, or any combination thereof. Infact, the storage system depicted in FIG. 4 may include the same, fewer,additional components as the storage systems described above.

The storage system (406) depicted in FIG. 4 is illustrated as includingcompute resources in the form of processing resources (416, 418, 420).The processing resources (416, 418, 420) may be embodied, for example,as physical resources such as one or more computer processors or asvirtualized resources such as a virtual machine, container, or someother virtualized component that can be used to execute a softwareapplication. The storage system (406) depicted in FIG. 4 is alsoillustrated as including shared storage resources in the form of storagedevices (430, 432, 434). The storage devices (430, 432, 434) may beembodied, for example, as one or more SSDs, HDDs, or other storagedevice.

The example method depicted in FIG. 4 includes receiving (408), from adata producer (402), a dataset (404). The data producer (402) depictedin FIG. 4 may be embodied, for example, as a simulation of a storagesystem that is executed in order to test hardware and softwarecomponents within the storage system that is being tested. Consider anexample in which software for a storage system is developed and testedutilizing a continuous integration (‘CI’) model in which all developerworking copies of system software are frequently merged to a sharedmainline. In such an example, such software may be tested by running asimulation of the storage system and running automated tests against thesimulated storage system, thereby generating a very large dataset (404)that consisted of log files, error logs, or some other form of data thatdescribes the operational state of the simulated storage system.

In the example method depicted in FIG. 4, receiving (408) the dataset(404) from the data producer (402) may be carried out, for example, byreceiving the dataset as it is generated by the data producer (402), byperiodically polling a location that the data producer (402) writes thedataset to, or in other ways. In fact, although the data producer (402)is depicted as residing outside of the storage system (406) in theembodiment depicted in FIG. 4, in other embodiments, the data producer(402) may actually be executing on the storage system (406) itself andmay even write the dataset directly to storage resources within thestorage system (406).

The example method depicted in FIG. 4 also includes storing (410),within the storage system (406), the dataset (404). In the examplemethod depicted in FIG. 4, the dataset (404) is depicted as being storedwithin the storage system (406) in multiple slices (424, 426, 428). Insuch an example, a first slice (424) may represent a first portion ofthe dataset, a second slice (426) may represent a second portion of thedataset, a third slice (428) may represent a third portion of thedataset, where RAID or RAID-like techniques are used to provide for dataredundancy in the event that one or more of the storage devices becomesunavailable. As such, parity data may also be maintained on the storagesystem (406), such that the dataset slices (424, 426, 428) and anyparity data form a RAID stripe. Readers will appreciate that the dataset(404) may be stored in other ways and that the dataset (404) may bestored (410) within the storage system (406) by the data producer (402)itself accessing the storage system (406), by system software and systemhardware on the storage system causing the dataset (404) (or the slicesthereof) to be written to storage devices (430, 432, 434) in the storagesystem (406), or in some other way.

The example method depicted in FIG. 4 also includes allocating (412)processing resources (416) to an analytics application (422). Theanalytics application (422) depicted in FIG. 4 may be embodied, forexample, as an application that examines datasets in order to drawconclusions about the information contained in the datasets, includingdrawing conclusions about the data producer (402). The analyticsapplication (422) may include artificial intelligence or machinelearning components, components that transform unstructured data intostructured or semi-structured data, big data components, and manyothers.

In the example method depicted in FIG. 4, allocating (412) processingresources (416) to an analytics application (422) may be carried out,for example, by allocating physical resources within the storage system(406) for use by the analytics application (422). For example, one ormore computer processors may be allocated for use by the analyticsapplication (422) such that the analytics application (422) is executingon the one or more computer processors. Alternatively, allocating (412)processing resources (416) to an analytics application (422) may becarried out by allocating virtualized physical resources within thestorage system (406) for use by the analytics application (422). Forexample, one or more virtual machines may be allocated for use by theanalytics application (422) such that the analytics application (422) isexecuting on the one or more virtual machines. Likewise, allocating(412) processing resources (416) to an analytics application (422) maybe carried out through the use of one or more containers, such that theanalytics application (422) is deployed and executed within the one ormore containers.

In the example method depicted in FIG. 4, executing (414) the analyticsapplication (422) on the processing resources (416) includes ingestingthe dataset (404) from the storage system (406). In such an example, theanalytics application (422) can ingest the dataset (404) from thestorage system (406) by reading the dataset (404) from the storagesystem (406) after it has been stored within the storage system (406).Readers will appreciate that, because the dataset (404) is stored withinshared storage, the analytics application (422) does not need to retaina copy of the dataset in storage (e.g., direct-attached storage) that isonly accessible by the processing resources that are being used toexecute the analytics application (422).

For further explanation, FIG. 5 sets forth a flow chart illustrating anadditional example method for executing a big data analytics pipeline ina storage system that includes compute resources and shared storageresources according to some embodiments of the present disclosure. Theexample method depicted in FIG. 5 is similar to the example methoddepicted in FIG. 4, as the example method depicted in FIG. 5 alsoincludes receiving (408) a dataset (404) from a data producer (402),storing (410) the dataset (404) within the storage system (406),allocating (412) processing resources (416) to an analytics application(422), and executing (414) the analytics application (422) on theprocessing resources (416), including ingesting the dataset (404) fromthe storage system (406).

The example method depicted in FIG. 5 also includes allocating (502)additional processing resources (418) to a real-time analyticsapplication (506). The real-time analytics application (506) may beembodied, for example, as an application that examines datasets in orderto draw conclusions about the information contained in the datasets,including drawing conclusions about the data producer (402). Much likethe analytics application (422), the real-time analytics application(506) may also include artificial intelligence or machine learningcomponents, components that transform unstructured data into structuredor semi-structured data, big data components, and many others. Unlikethe analytics application (422), however, the real-time analyticsapplication (506) examines datasets as they are generated, rather thananalyzing datasets that are more historical in nature.

In the example method depicted in FIG. 5, allocating (502) additionalprocessing resources (418) to the real-time analytics application (506)may be carried out, for example, by allocating physical resources withinthe storage system (406) for use by the real-time analytics application(506). For example, one or more computer processors may be allocated foruse by the real-time analytics application (506) such that the real-timeanalytics application (506) is executing on the one or more computerprocessors. Alternatively, allocating (502) additional processingresources (418) to the real-time analytics application (506) may becarried out by allocating virtualized physical resources within thestorage system (406) for use by the real-time analytics application(506). For example, one or more virtual machines may be allocated foruse by the real-time analytics application (506) such that the real-timeanalytics application (506) is executing on the one or more virtualmachines. Likewise, allocating (502) additional processing resources(418) to the real-time analytics application (506) may be carried outthrough the use of one or more containers, such that the real-timeanalytics application (506) is deployed and executed within the one ormore containers.

In the example method depicted in FIG. 5, executing (504) the real-timeanalytics application (506) on the additional processing resources caninclude ingesting the dataset (404) prior to storing (410) the dataset(404) within the storage system (406). In such an example, the real-timeanalytics application (506) may, in effect, be part of the data path asthe dataset (404) is fed to the real-time analytics application (506)upon receipt by the storage system. Readers will appreciate that inother embodiments, the real-time nature of the real-time analyticsapplication (506) may be enforced in other ways. For example, thereal-time analytics application (506) may only consume the portions ofthe dataset (404) that have been produced within some threshold (e.g.,the real-time analytics application (506) may only consume portions ofthe dataset (404) that have been produced within the last 30 minutes)while the analytics application (422) consumes all other portions of thedataset (404). Readers will appreciate that, because the dataset (404)is stored within shared storage, the analytics application (422) and thereal-time analytics application (506) do not need to retain copies ofthe dataset in storage (e.g., direct-attached storage) that is onlyaccessible by the processing resources that are being used to executethe analytics application (422) or the real-time analytics application(506). In fact, the analytics application (422) and the real-timeanalytics application (506) may be reading their respective portions ofthe dataset (404) from a single copy of the dataset (404) that is storedwithin the storage system (406).

For further explanation, FIG. 6 sets forth a flow chart illustrating anadditional example method for executing a big data analytics pipeline ina storage system that includes compute resources and shared storageresources according to some embodiments of the present disclosure. Theexample method depicted in FIG. 6 is similar to the example methoddepicted in FIG. 4, as the example method depicted in FIG. 6 alsoincludes receiving (408) a dataset (404) from a data producer (402),storing (410) the dataset (404) within the storage system (406),allocating (412) processing resources (416) to an analytics application(422), and executing (414) the analytics application (422) on theprocessing resources (416), including ingesting the dataset (404) fromthe storage system (406).

In the example method depicted in FIG. 6, the dataset (404) includes logfiles (602) describing one or more execution states of a computingsystem. In the example depicted in FIG. 6, the computing system whoseexecution states are described in the log files (602) may be embodied,for example, as a storage system that is being tested as a part of asoftware development and testing process. In such an example, the logfiles (602) may include information describing how the storage system isoperating in response to a test suite being executed on the storagesystem.

In the example method depicted in FIG. 6, executing (414) the analyticsapplication (422) on the processing resources (416) can includeevaluating (604) the log files (602) to identify one or more executionpatterns associated with the computing system. Continuing with theexample described above in which the computing system whose executionstates are described in the log files (602) is embodied as a storagesystem that is being tested as a part of a software development andtesting process, the log files (602) may include information such as theamount of time that each read or write took to complete, informationthat indicates the number of IOPS that were being serviced, and so on.In such an example, evaluating (604) the log files (602) to identify oneor more execution patterns associated with the computing system mayinclude examining the log files (602) to determine the average amount oftime that each read or write took to complete and whether the averageamount of time that each read or write took to complete was acceptable,examining the log files (602) to determine whether the average amount oftime that each read or write took to complete was trending up or down,examining the log files (602) to determine whether the average amount oftime that each read or write took to complete was acceptable at varyinglevels of load, and so on. In fact, the one or more execution patternsassociated with the computing system can focus on a wide range ofmetrics and can be used to examine many aspects of system health, systemoperation, and so on.

In the example method depicted in FIG. 6, evaluating (604) the log files(602) to identify one or more execution patterns associated with thecomputing system can include comparing (606) fingerprints associatedwith known execution patterns to information contained in the log files(602). In such an example, the fingerprints that are associated withknown execution patterns may include information such as, for example,ranges for one or more metrics that are associated with some particularknown execution pattern, a pattern of alerts that are associated withsome particular known execution pattern, and so on. For example, aparticular sequence of alerts may have been identified as beingassociated with a computing system that is about to fail, and as such, afingerprint may exist that includes the particular sequence of alerts,such that the log files (602) may be examined to determine whether theparticular sequence of alerts contained in the fingerprint are alsofound in the log files (602), thereby indicating that the system undertest may be about to fail. In the example method depicted in FIG. 6, thefingerprints associated with known execution patterns may includemulti-line fingerprints, where multiple lines of a log file are examinedto determine whether the log files contain a particular fingerprint.Likewise, fingerprints can include sequences and combinations of eventssuch that a match is identified only if a sequence or combination ofevents is identified in the log files (602).

For further explanation, FIG. 7 sets forth a flow chart illustrating anadditional example method for executing a big data analytics pipeline ina storage system that includes compute resources and shared storageresources according to some embodiments of the present disclosure. Theexample method depicted in FIG. 7 is similar to the example methoddepicted in FIG. 4, as the example method depicted in FIG. 7 alsoincludes receiving (408) a dataset (404) from a data producer (402),storing (410) the dataset (404) within the storage system (406),allocating (412) processing resources (416) to an analytics application(422), and executing (414) the analytics application (422) on theprocessing resources (416), including ingesting the dataset (404) fromthe storage system (406).

In the example method depicted in FIG. 7, storing (410) the dataset(404) within the storage system (406) can include organizing (708) thedataset into an indexed directory structure. In such an example, theindexed directory structure may be created by storing data in such a wayso as to facilitate fast and accurate searching of the directorystructure. In fact, large datasets such as the log files that aregenerated during testing may be generated with names that include thingslike a timestamp, an identification of the cluster that generated thelog file, and so on and organized in the directory structure accordingto some indexing scheme. As such, the indexed file system mayessentially be used as a database that can be quickly searched, butwithout the limitations of a database that causes databases to performpoorly on very, very large datasets.

In the example method depicted in FIG. 7, receiving (408) a dataset(404) from a data producer (402) can include receiving (702) anunstructured dataset. In the example method depicted in FIG. 7, theunstructured dataset may include unstructured data that either does nothave a pre-defined data model or is not organized in a pre-definedmanner. Such unstructured information, as is often contained in logfiles, is typically text-heavy for ease of understanding by a human(e.g., a system administrator) that is tasked with reviewing the logfiles. Unstructured data, however, frequently has irregularities andambiguities that make it difficult to understand using traditionalprograms as compared to structured data such as data stored in fieldedform in databases or annotated in documents.

The example method depicted in FIG. 7 also includes converting (704) theunstructured dataset into a structured dataset. In the example methoddepicted in FIG. 7, a structured dataset includes structured (orsemi-structured) data where data can reside in a fixed field within arecord or file. In such an example, the structured dataset can includeinformation with a high degree of organization, such that inclusion in arelational database (or similar data repository) is seamless and readilysearchable by simple, straightforward search engine algorithms or othersearch operations.

In the example method depicted in FIG. 7, converting (704) theunstructured dataset into a structured dataset may be carried out, forexample, through the use of techniques such as data mining, naturallanguage processing (NLP), and text analytics to find patterns in, orotherwise interpret, the unstructured data. Techniques for structuringtext can involve tagging unstructured data with metadata. In suchembodiments, software that creates machine-processable structure canutilize the linguistic, auditory, and visual structure that exist invarious forms of human communication and algorithms can infer thisinherent structure from text, for instance, by examining wordmorphology, sentence syntax, and so on. In such an example, unstructuredinformation can be enriched and tagged to address ambiguities andrelevancy-based techniques then used to facilitate search and discovery.In the example method depicted in FIG. 7, storing (410) the dataset(404) within the storage system (406) can include storing (706) thestructured dataset within the storage system.

For further explanation, FIG. 8A sets forth a diagram illustrating anexample computer architecture for implementing an artificialintelligence and machine learning infrastructure (800) (also referred toherein as an ‘artificial infrastructure’) that is configured to fitwithin a single chassis (not depicted) according to some embodiments ofthe present disclosure. While in this example, the communication fabricincludes a set of network switches (803) for interconnecting a networkappliance (800A) with the one or more GPU system(s) (801), and for theartificial intelligence and machine learning infrastructure (800) tocommunicate with one or more computing devices over one or morenetworks, in other implementations, the communication fabric may bearchitected to define different communication paths between the networkappliance (800A) and the GPU system(s) (801), and one or more computingdevices or host computer systems.

In this example artificial intelligence and machine learninginfrastructure (800), the network appliance (800A) may be a storagesystem that includes one or more storage devices, and the GPU systems(801) may be, in this example, five (5) NVIDIA DGX-1 GPU systems. Inthis example, the network appliance (800A) may be connected to twoswitches (803) using, respectively, four, 100 GbE connections, whereeach switch (801) may be connected to each GPU system (801) by two 100GbE connections—resulting in each of the GPU system (801) having four(4) 100 GbE connections to the network appliance (800A).

For further explanation, FIG. 8B sets forth a flow chart illustrating anadditional example method for executing a big data analytics pipeline ina storage system that includes compute resources and shared storageresources according to some embodiments of the present disclosure. Theexample method depicted in FIG. 8B is similar to the example methoddepicted in FIG. 4, as the example method depicted in FIG. 8B alsoincludes receiving (408) a dataset (404) from a data producer (402),storing (410) the dataset (404) within the storage system (406),allocating (412) processing resources (416) to an analytics application(422), and executing (414) the analytics application (422) on theprocessing resources (416), including ingesting the dataset (404) fromthe storage system (406).

In the example method depicted in FIG. 8B, receiving (408) a dataset(404) from a data producer (402) can include receiving (806), from aplurality of data producers (402, 802), a dataset (404, 804) that isunique to each data producer. The data producers (402, 802) depicted inFIG. 8B may be embodied, for example, as simulations of multiple storagesystem that is executed in order to test hardware and softwarecomponents within the storage system that is being tested. For example,the first data producer (402) may be a simulated version of a firststorage system and the second data producer (802) may be a simulation ofa second storage system. In the example method depicted in FIG. 8B,receiving (806) a dataset (404, 804) that is unique to each dataproducer may be carried out, for example, by receiving each dataset asit is generated by the respective data producer (402, 802), byperiodically polling a location that each data producer (402, 802)writes the dataset to, or in other ways. In fact, although the dataproducers (402, 802) are depicted as residing outside of the storagesystem (406) in the embodiment depicted in FIG. 8B, in otherembodiments, one or more of the data producers (402, 802) may actuallybe executing on the storage system (406) itself and may even write thedataset directly to storage resources within the storage system (406).

In the example method depicted in FIG. 8B, storing (410) the dataset(404) within the storage system (406) can include storing (808), withinthe storage system (406), each unique dataset (404, 804). In the examplemethod depicted in FIG. 8B, each unique dataset (404, 804) is depictedas being stored within the storage system (406) in multiple slices (424,426, 428, 816, 818, 820). For example, a first dataset (404) is storedas a first set of slices (424, 426, 428) and a second dataset (804) isstored as a second set of slices (816, 818, 820). In such an example,each slice may represent a distinct portion of the dataset, where RAIDor RAID-like techniques are used to provide for data redundancy that oneor more of the storage devices becomes unavailable. As such, parity datamay also be maintained on the storage system (406), such that thedataset slices (424, 426, 428, 816, 818, 820) and any parity data form aRAID stripe. Readers will appreciate that each dataset (404, 804) may bestored in other ways and that each dataset (404, 804) may be stored(808) within the storage system (406) by the data producer (402, 802)itself accessing the storage system (406), by system software and systemhardware on the storage system causing each dataset (404, 804) (or theslices thereof) to be written to storage devices (430, 432, 434) in thestorage system (406), or in some other way.

In the example method depicted in FIG. 8B, allocating (412) processingresources (416) to an analytics application (422) can include allocating(810) unique processing resources (416, 418) to each of a plurality ofanalytics applications (422, 814). In the example method depicted inFIG. 8B, allocating (810) unique processing resources (416, 418) to eachof a plurality of analytics applications (422, 814) may be carried out,for example, by allocating physical resources within the storage system(406) for use by the analytics applications (422, 814). For example, afirst computer processor may be allocated for use by a first analyticsapplication (422) such that the analytics application (422) is executingon the first computer processor and a second computer processor may beallocated for use by a second analytics application (814) such that theanalytics application (814) is executing on the second computerprocessor. Alternatively, allocating (810) unique processing resources(416, 418) to each of a plurality of analytics applications (422, 814)may be carried out by allocating virtualized physical resources withinthe storage system (406) for use by each of the analytics applications(422, 814). For example, a first set of virtual machines may beallocated for use by a first analytics application (422) such that theanalytics application (422) is executing on the first set of virtualmachines and a second set of virtual machines may be allocated for useby a second analytics application (814) such that the analyticsapplication (814) is executing on the second set of virtual machines.Likewise, allocating (810) unique processing resources (416, 418) toeach of a plurality of analytics applications (422, 814) may be carriedout through the use of containers, such that a first analyticsapplication (422) is deployed and executed within a first container anda second analytics application (814) is deployed and executed within asecond container.

In the example method depicted in FIG. 8B, executing (414) the analyticsapplication (422) on the processing resources (416) can includeexecuting (812) the plurality of analytics applications (422, 814) onthe processing resources (416, 418), including ingesting each uniquedataset (404, 804) from the storage system (406). In such an example, afirst analytics application (422) can ingest a first dataset (404) fromthe storage system (406) by reading the dataset (404) from the storagesystem (406) after it has been stored within the storage system (406)and a second analytics application (814) can ingest a second dataset(804) from the storage system (406) by reading the dataset (804) fromthe storage system (406) after it has been stored within the storagesystem (406). Readers will appreciate that, because the dataset (404) isstored within shared storage, neither analytics application (422, 814)will need to retain a copy of the dataset in storage (e.g.,direct-attached storage) that is only accessible by the processingresources that are being used to execute the analytics application (422,814).

For further explanation, FIG. 9 sets forth a flow chart illustrating anadditional example method for executing a big data analytics pipeline ina storage system that includes compute resources and shared storageresources according to some embodiments of the present disclosure. Theexample method depicted in FIG. 9 is similar to the example methoddepicted in FIG. 4, as the example method depicted in FIG. 9 alsoincludes receiving (408) a dataset (404) from a data producer (402),storing (410) the dataset (404) within the storage system (406),allocating (412) processing resources (416) to an analytics application(422), and executing (414) the analytics application (422) on theprocessing resources (416), including ingesting the dataset (404) fromthe storage system (406).

The example method depicted in FIG. 9 also includes detecting (902) thatthe analytics application (422) has ceased executing properly. Detecting(902) that the analytics application (422) has ceased executing properlymay be carried out, for example, by detecting that the analyticsapplication (422) has crashed, by detecting that the analyticsapplication (422) has become unresponsive, by detecting that theprocessing resources that the analytics application (422) is executingon have become unavailable, or in other ways. In such an example, thestorage system (406) can detect (902) that the analytics application(422) has ceased executing properly through the use of a heartbeatmechanism, by detecting an absence of messaging or reporting from theanalytics application (422), or through the use of a similar mechanism.

The example method depicted in FIG. 9 also includes allocating (904)second processing resources (418) to the analytics application (422). Inthe example method depicted in FIG. 9, allocating (904) secondprocessing resources (418) to the analytics application (422) may becarried out, for example, by allocating physical resources within thestorage system (406) for use by the analytics application (422). Forexample, one or more computer processors may be allocated for use by theanalytics application (422) such that the analytics application (422) isexecuting on the one or more computer processors. Alternatively,allocating (904) second processing resources (418) to the analyticsapplication (422) may be carried out by allocating virtualized physicalresources within the storage system (406) for use by the analyticsapplication (422). For example, one or more virtual machines may beallocated for use by the analytics application (422) such that theanalytics application (422) is executing on the one or more virtualmachines. Likewise, allocating (904) second processing resources (418)to the analytics application (422) may be carried out through the use ofone or more containers, such that the analytics application (422) isdeployed and executed within the one or more containers.

The example method depicted in FIG. 9 also includes executing (906) theanalytics application (422) on the second processing resources (418),including ingesting the dataset (404). In such an example, the analyticsapplication (422) can ingest the dataset (404) from the storage system(406) by reading the dataset (404) from the storage system (406) afterit has been stored within the storage system (406). Readers willappreciate that, because the dataset (404) is stored within sharedstorage, the analytics application (422) does not need to retain a copyof the dataset in storage (e.g., direct-attached storage) that is onlyaccessible by the processing resources that are being used to executethe analytics application (422).

For further explanation, FIG. 10 sets forth a flow chart illustrating anadditional example method for executing a big data analytics pipeline ina storage system that includes compute resources and shared storageresources according to some embodiments of the present disclosure. Theexample method depicted in FIG. 10 is similar to the example methoddepicted in FIG. 4, as the example method depicted in FIG. 10 alsoincludes receiving (408) a dataset (404) from a data producer (402),storing (410) the dataset (404) within the storage system (406),allocating (412) processing resources (416) to an analytics application(422), and executing (414) the analytics application (422) on theprocessing resources (416), including ingesting the dataset (404) fromthe storage system (406).

The example method depicted in FIG. 10 also includes detecting (1002)that the analytics application (422) needs additional processingresources. Detecting (1002) that the analytics application (422) needsadditional processing resources may be carried out, for example, bydetecting that the processing resources upon which the analyticsapplication (422) is executing are fully utilized or that utilizationhas reached a threshold level, by detecting that the analyticsapplication (422) has become unresponsive, slow to respond to messages,slow to report findings, or is otherwise exhibiting some behavior thatis associated with a lack of sufficient processing resources, or in someother way.

The example method depicted in FIG. 10 also includes allocating (1004)additional processing resources (418) to the analytics application(422). In the example method depicted in FIG. 10, allocating (1004)additional processing resources (418) to the analytics application (422)may be carried out, for example, by allocating additional physicalresources within the storage system (406) for use by the analyticsapplications (422). For example, a first computer processor mayinitially be allocated for use by the analytics application (422) suchthat the analytics application (422) is executing on the first computerprocessor. In such an example, a second computer processor mayadditionally be allocated for use by the analytics application (422)such that the analytics application (422) is executing on both the firstcomputer processor and the second computer processor. Alternatively,allocating (1004) additional processing resources (418) to the analyticsapplication (422) may be carried out by allocating additionalvirtualized physical resources within the storage system (406) for useby the analytics applications (422). For example, a first set of virtualmachines may be initially allocated for use by the analytics application(422) such that the analytics application (422) is executing on thefirst set of virtual machines. In such an example, a second set ofvirtual machines may be additionally allocated for use by the analyticsapplication (422) such that the analytics application (422) is executingon both the first set of virtual machines and the second set of virtualmachines. Likewise, allocating (1004) additional processing resources(418) to the analytics application (422) may be carried out through theuse of containers, such that an analytics application (422) is initiallydeployed and executed within a first container and a second container issubsequently utilized to support the analytics application (422).

The example method depicted in FIG. 10 also includes executing (1006)the analytics application (422) on the additional processing resources(418). Readers will appreciate that although the embodiments describedabove relate to embodiments where instances of the analytics application(422) are executed on multiple processing resources (416, 418), in otherembodiments different processing resources (416, 418) instead be used toexecute various portions of the analytics application (422). Forexample, a first portion of the analytics application (422) may executeon a first set of processing resources (416) and a second portion of theanalytics application (422) may execute on a second set of processingresources (418). Readers will further appreciate that the shared natureof the storage that is utilized by the analytics application (422)results in more efficient scalability, as the application can be scaledup (i.e., more processing resources can be given to the analyticsapplication) without needing to copy the dataset, send the dataset overa network connection, and so on as would be required if the analyticsapplication (422) were executing on a processing node withdirect-attached storage where each node maintained its own copy of thedataset.

As described above, the analytics application (422) may includeartificial intelligence or machine learning components. In fact, theanalytics application (422) may be an AI application. Data is the heartof modern AI and deep learning algorithms. Before training can begin,one problem that must be addressed revolves around collecting thelabeled data that is crucial for training an accurate AI model. A fullscale AI deployment may be required to continuously collect, clean,transform, label, and store large amounts of data. Adding additionalhigh quality data points directly translates to more accurate models andbetter insights. Data samples may undergo a series of processing stepsincluding, but not limited to: 1) ingesting the data from an externalsource into the training system and storing the data in raw form, 2)cleaning and transforming the data in a format convenient for training,including linking data samples to the appropriate label, 3) exploringparameters and models, quickly testing with a smaller dataset, anditerating to converge on the most promising models to push into theproduction cluster, 4) executing training phases to select randombatches of input data, including both new and older samples, and feedingthose into production GPU servers for computation to update modelparameters, and 5) evaluating including using a holdback portion of thedata not used in training in order to evaluate model accuracy on theholdout data. This lifecycle may apply for any type of parallelizedmachine learning, not just neural networks or deep learning. Forexample, standard machine learning frameworks may rely on CPUs insteadof GPUs but the data ingest and training workflows may be the same.Readers will appreciate that a single shared storage data hub creates acoordination point throughout the lifecycle without the need for extradata copies among the ingest, preprocessing, and training stages. Rarelyis the ingested data used for only one purpose, and shared storage givesthe flexibility to train multiple different models or apply traditionalanalytics to the data.

Readers will appreciate that each stage in the AI data pipeline may havevarying requirements from the data hub (e.g., the storage system orcollection of storage systems). Scale-out storage systems must deliveruncompromising performance for all manner of access types andpatterns—from small, metadata-heavy to large files, from random tosequential access patterns, and from low to high concurrency. Thestorage systems described above may serve as an ideal AI data hub as thesystems may service unstructured workloads. In the first stage, data isideally ingested and stored on to the same data hub that followingstages will use, in order to avoid excess data copying. The next twosteps can be done on a standard compute server that optionally includesa GPU, and then in the fourth and last stage, full training productionjobs are run on powerful GPU-accelerated servers. Often, there is aproduction pipeline alongside an experimental pipeline operating on thesame dataset. Further, the GPU-accelerated servers can be usedindependently for different models or joined together to train on onelarger model, even spanning multiple systems for distributed training.If the shared storage tier is slow, then data must be copied to localstorage for each phase, resulting in wasted time staging data ontodifferent servers. The ideal data hub for the AI training pipelinedelivers performance similar to data stored locally on the server nodewhile also having the simplicity and performance to enable all pipelinestages to operate concurrently.

A data scientist works to improve the usefulness of the trained modelthrough a wide variety of approaches: more data, better data, smartertraining, and deeper models. In many cases, there will be teams of datascientists sharing the same datasets and working in parallel to producenew and improved training models. Often, there is a team of datascientists working within these phases concurrently on the same shareddatasets. Multiple, concurrent workloads of data processing,experimentation, and full-scale training layer the demands of multipleaccess patterns on the storage tier. In other words, storage cannot justsatisfy large file reads, but must contend with a mix of large and smallfile reads and writes. Finally, with multiple data scientists exploringdatasets and models, it may be critical to store data in its nativeformat to provide flexibility for each user to transform, clean, and usethe data in a unique way. The storage systems described above mayprovide a natural shared storage home for the dataset, with dataprotection redundancy (e.g., by using RAID6) and the performancenecessary to be a common access point for multiple developers andmultiple experiments. Using the storage systems described above mayavoid the need to carefully copy subsets of the data for local work,saving both engineering and GPU-accelerated servers use time. Thesecopies become a constant and growing tax as the raw data set and desiredtransformations constantly update and change.

Readers will appreciate that a fundamental reason why deep learning hasseen a surge in success is the continued improvement of models withlarger data set sizes. In contrast, classical machine learningalgorithms, like logistic regression, stop improving in accuracy atsmaller data set sizes. As such, the separation of compute resources andstorage resources may also allow independent scaling of each tier,avoiding many of the complexities inherent in managing both together. Asthe data set size grows or new data sets are considered, a scale outstorage system must be able to expand easily. Similarly, if moreconcurrent training is required, additional GPUs or other computeresources can be added without concern for their internal storage.Furthermore, the storage systems described above may make building,operating, and growing an AI system easier due to the random readbandwidth provided by the storage systems, the ability to of the storagesystems to randomly read small files (50 KB) high rates (meaning that noextra effort is required to aggregate individual data points to makelarger, storage-friendly files), the ability of the storage systems toscale capacity and performance as either the dataset grows or thethroughput requirements grow, the ability of the storage systems tosupport files or objects, the ability of the storage systems to tuneperformance for large or small files (i.e., no need for the user toprovision filesystems), the ability of the storage systems to supportnon-disruptive upgrades of hardware and software even during productionmodel training, and for many other reasons.

Small file performance of the storage tier may be critical as many typesof inputs, including text, audio, or images will be natively stored assmall files. If the storage tier does not handle small files well, anextra step will be required to pre-process and group samples into largerfiles. Storage, built on top of spinning disks, that relies on SSD as acaching tier, may fall short of the performance needed. Because trainingwith random input batches results in more accurate models, the entiredata set must be accessible with full performance. SSD caches onlyprovide high performance for a small subset of the data and will beineffective at hiding the latency of spinning drives.

Readers will further appreciate that in some embodiments of the presentdisclosure, big data services may be built-in to the shared storagesystem such that big data analytics, machine learning, artificialintelligence, and other functionality can be offered as a service. Insuch an example, big data analytics applications, machine learningapplications, artificial intelligence applications, and others may beincorporated into the same (or otherwise accessible) codebase as systemsoftware that controls the operation of the storage system, such thatthe interactions between system hardware, system software, and theadditional applications can be optimized. Furthermore, these additionalapplications can be offered as cogs in an analytics stack to assistusers of the storage system in the development and deployment of bigdata analytics applications, machine learning applications, artificialintelligence applications, and similar applications.

Readers will further appreciate that in some embodiments of the presentdisclosure, idempotent operations may allow for arbitrary reruns andmodification of the analytics pipeline. Through the use of orchestrationand containerization related concepts described above, a storage systemmay present a software layer that runs in idempotent chunks such that ahands-off approach to recovery management may be taken. In such anexample, if a dependency graph of jobs were in place where each job hadsome level of idempotency, changes could be made to a job anywhere inthe graph and determinations could be made regarding what jobs wouldneed to be rerun to complete recovery. Furthermore, because additionalcompute resources may be allocated, the system could automate datachanges or execute them from a simple form.

Readers will further appreciate that in some embodiments of the presentdisclosure, with the addition of heartbeat events or expected datapatterns, a storage system could essentially run continuous testing on adata pipeline, take recovery actions, and rerun steps if heartbeats aremissing. Because there are many things that can go wrong when analyticsare being performed in an environment that includes many hosts withdifferent network and rack configurations, errors can occur and may behard to detect. Even if errors are not common, they may be hard todetect and hard to trace back to the root cause. As such, embodimentsdescribed herein may add continuous monitoring to the outputs of thepipeline by adding fingerprints to be expected, regular events that areexpected to occur, and information may be persisted to capture actualsystem performance. Once anomalies are found, the storage system mayattempt to re-collect data, rerun jobs, issue alerts if anomalies arestill detected, and otherwise support a self-healing big data analyticspipeline.

Readers will appreciate that although the embodiments described aboverelate to embodiments where steps may appear to occur according to someorder, no ordering is actually required unless explicitly stated.Furthermore, in some embodiments, steps that appear in different figuresmay actually occur in a single embodiment. That is, the organization ofsteps that is included above is for ease of explanation, and in no waylimits the various embodiments of the concepts described herein. Infact, embodiments of the present disclosure may include any combinationof the steps described above and claimed herein. Likewise, embodimentsof the present disclosure may be implemented on any of the storagesystems, or any combination therefore, described herein.

For further explanation, FIG. 11A sets forth a diagram illustrating anexample artificial intelligence and machine learning infrastructure(1100) according to some embodiments of the present disclosure. Asdepicted, the artificial and machine learning infrastructure (1100) maybe embodied or implemented entirely within a single chassis (1101). Insome examples, the chassis (1101) may be implemented according to thedimensions of a standard rack within a data center—where the singlechassis (1101) includes the one or more storage systems (1120), such asany of the storage systems described above or any combination of suchstorage systems, and where the single chassis (1101) may further includeone or more GPU systems (1130A-1130N).

As one example embodiment, the chassis (1101) may include storagesystem(s) (1120) implemented as one or more Pure™ FlashBlade™ storagesystems of flash storage devices or one or more other types of flashstorage devices, and the one or more GPU systems (1130A-1130N) may beimplemented as one or more NVIDIA™ DGX-1™ GPU architectures or as one ormore other GPU architectures. In this example, the GPU architectures mayfurther include multiple GPUs and one or more CPUs—where the GPUarchitecture may further include onboard system memory. However, inother examples, different combinations of storage systems and GPUarchitectures may be implemented as an integrated artificialintelligence and machine learning infrastructure within the singlechassis (1101).

Further, in some examples, the single chassis (1101) may include one ormore length, width, and depth physical dimensions that are smaller orlarger than a standard rack size—for example the single chassis (1101)may be a half rack or smaller. In this example, a rack may be about 42U, or 6 feet (180 cm) in height, where a “U” unit of measure may bedefined as 44.50 millimeters (1.752 in.), and where the rack width maybe 19 inches (482.60 mm), and where the depth may be 36 inches (914.40mm).

In this embodiment, the height (1102) of the storage system(s) (1120)may be 4 U, where the width (1104) and depth (1106) are defined to fitwithin the physical dimensions of the chassis (1101). Similarly, each ofthe GPU system(s) (1130A-1130N) may be of the same or differentdimensions, where an example height (1108) may be defined to be 1 U or 2U, and where the width (1110) and depth (1112) may be defined to fitwithin the physical dimensions of the chassis (1101).

For further explanation, FIG. 11B sets forth a diagram illustrating anexample computer architecture for implementing an artificialintelligence and machine learning infrastructure (1100) within a singlechassis (1101) according to some embodiments of the present disclosure.While in this example, the communication fabric includes a tiered set ofnetwork switches (1132A-1132C) for interconnecting the storage system(s)(1120) with the one or more GPU system(s) (1130A-1130N), and for theartificial intelligence and machine learning infrastructure (1100) tocommunicate with one or more computing devices (1129) over one or morenetworks (1131), in other implementations, the communication fabric maybe architected to define different communication paths between thestorage system(s) (1120) and the GPU system(s) (1130A-1130N), and one ormore computing devices or host computer systems.

In some implementations, the artificial intelligence and machinelearning infrastructure (1100) communication fabric may implement aremote direct memory access (RDMA) protocol over converged ethernet(RoCE) fabric, where such a communication fabric implements directmemory access from a source computer system to a target computer systemwithout involvement of an operating system on either the source ortarget computer system—where, depending on the direction of acommunication path, the storage system(s) (1120) may be a source ortarget computer system and the GPU systems (1130A-1130N) may be a sourceor target computer system.

In this example, given the communication fabric depicted in artificialintelligence and machine learning infrastructure (1100)—where thecommunication fabric may implement multiple parallel communicationchannels through each switch (1132A-1132C)—and based on the storagesystem(s) (1120) including multiple storage devices, where each storagedevice may include one or more controllers that may each communicatedirectly with one or more of the GPUs within GPU systems(s)(1130A-1130N), artificial intelligence and machine learninginfrastructure (1100) may implement multiple, parallel high-speedcommunication paths between different combinations of storage deviceswithin the storage system(s) (1120) and computing elements of the GPUsystem(s) (1130A-1130N).

In other example implementations, the communication fabric may implementother network communication protocols, including the communicationprotocols discussed above with respect to the storage system (340)described in FIGS. 1A-3B, including InfiniBand and iWARP.

In some implementations, artificial intelligence and machine learninginfrastructure (1100) may be scaled to include additional storagesystems or additional GPU systems within the same chassis (1101), wherethe communication fabric may be similarly scaled to connect theadditional storage systems and/or GPU systems via network switches(1132A-1132C). In other cases, the communication fabric may be scaled toinclude additional network switches or additional tiers to thecommunication fabric.

For further explanation, FIG. 11C sets forth a diagram illustrating anexample implementation of an artificial intelligence and machinelearning infrastructure software stack (1105) according to someembodiments of the present disclosure.

As depicted in FIG. 11C, the artificial intelligence and machinelearning infrastructure software stack (1105) may be implementedentirely within the artificial intelligence and machine learninginfrastructure (110) depicted in FIGS. 11A and 11B. Further, theartificial intelligence and machine learning infrastructure softwarestack (1105) may include multiple software layers, including amulti-node training (1107A) layer, a deep learning framework (1107B)layer, a containerization (1107C) layer, a scale-out GPU compute (1107D)layer, a scale-out files/object protocol (1107E) layer, and a scale-outstorage (1107F) layer, among other potential software layers notdepicted in FIG. 11C.

The multi-node training (1107A) layer may implement a scaling toolkit,or a configuration interface, that provides specifications formulti-node training within the artificial intelligence and machinelearning infrastructure (1100). The scaling toolkit may be used tospecify configuration settings between the storage system(s) (1120), theGPU systems (1130A-1130N), and network components, including networkswitches (1132A-132C) of the communication fabric.

The deep learning framework (1107B) layer may implement deep learningframeworks such as Caffe, Caffe2, mxnet, pytorch, torch, among otherdeep learning frameworks. Further, each deep learning frameworkimplemented at the deep learning framework (1107B) layer may bedelivered as a container to the containerization (1107C) layer. Further,the containerization (1107C) layer may implement GPU drivers forcommunicating with the GPUs of the scale-out GPU compute (1107D) layer,and the containerization (1107C) layer may also implement NVIDIA™Docker™.

The scale-out GPU compute (1107D) layer may be implemented by the GPUsystems (1130A-1130N), and the scale-out GPU compute (1107D) layer mayprovide an interface for assigning jobs, sending or receiving data,adding or removing GPU systems, or for configuring one or more of theGPUs within the GPU systems (1130A-1130N). In some examples, thefunctionality provided by the scale-out GPU compute (1107D) layer may beprovided to layers above and below via an API specifying commands andparameters for each supported functionality for the corresponding layerinterface.

The scale-out file/object protocols (1107E) layer may provide an API fora logical data handling layer, such as a file system that provides filesystems operations for creating, deleting, moving, copying, or otherstandard file system operations. In some examples, the scale-outfile/objects protocols (1107E) layer may provide block level access, ordata access according to a specified range or ranges of bytes.

The scale-out storage (1107F) layer may be implemented by the storagesystem(s) (1130), and the scale-out storage (1107F) layer may provide aninterface for any storage system functionality described above withrespect to FIGS. 1A-3B, including reading, writing, erasing, orconfiguring storage device settings, or configuring garbage collection,or for programming the one or more controllers implemented by each ofthe included storage systems or storage devices. For example, thescale-out storage (1107F) layer may provide an API for performinginput/output operations on physical data stored within the memorycomponents of the storage system.

In some examples, the scale-out file/object protocol (1107E) layer andthe scale-out storage (1107F) layer, individually or in combination, mayprovide for implementations of a virtual memory environment, memorymanagement, or one or more types of files systems or methods forcreating, deleting, copying, reading, or writing files or objects.

For further explanation, FIG. 11D sets forth a flow chart illustratingan example method for interconnecting a graphical processing unit layerand a storage layer of an artificial intelligence and machine learninginfrastructure according to some embodiments of the present disclosure.Although depicted in less detail, the example artificial intelligenceand machine learning infrastructure (1100) may be similar to theimplementations described above with reference to FIGS. 11A-11C, or anycombination thereof.

In this example, a data path may be considered use of one or moreprotocols for a communication path directly between the scale-out GPUcompute (1107D) layer and the scale-out storage (1107F) layer. In otherexamples, the data path may be considered use of one or more protocolsfor implementing a communication path between the scale-out GPU compute(1107D) layer, the scale-out files/object protocols (1107E) layer, andthe scale-out storage (1107F) layer—where the scale-out GPU compute(1107D) layer communicates to and from the scale-out files/objectprotocols (1107E) layer via one or more APIs, and where the scale-outfiles/object protocols (1107E) layer communicates with the scale-outstorage (1107F) layer via one or more APIs. While in this example, thedata path includes the bottom three layers of the artificialintelligence and machine learning infrastructure software stack (1107D,1107E, 1107F), in other examples, the data path may include one or moreother software layers, including the multi-node training (1107A) layer,the deep learning framework (1107B) layer, and/or the containerization(1107C) layer.

In this example, a definition of a data path may be based on theintegration of the software stack as depicted and described above withrespect to FIGS. 11A-11C. For example, the scale-out storage (1107F) maybe configured to provide an API call that specifies for the scale-outstorage (1107F) layer to implement a data transformation or dataanalysis on stored data—where the result of the API call is a result ofthe data transformation or data analysis performed by the scale-outstorage (1107F) layer, and where the scale-out storage (1107F) layerimplements the data analysis or data transformation using one or morecontrollers for one or more storage devices.

In some examples, the API provided by the scale-out storage (1107F)layer may provide data analysis or data transformation functionality orroutines that include one or more of: JPEG decode, shuffle, combiningfiles, and/or reshaping matrices/tensors. In general, and in dependenceupon the controllers of the storage devices of the storage system (1130)being configured to perform any type of general computing functionalityas described above with reference to FIGS. 1A-3B, the API provided bythe scale-out storage (1107F) layer may provide an API interface for anytype of data analysis or data transformation. As one example, thescale-out storage (1107F) layer may provide an API call that instructsthe scale-out storage (1107F) layer to select a subset of data thatmatches a particular category.

Further, in some examples, the API provided by the scale-out storage(1107F) layer may include an API call that takes as a parameter functioncode, or a reference to function code, where one or more controllers ofthe storage system(s) (1130) of the scale-out storage (1107F) layer mayexecute the function code to perform a specified data analysis or datatransformation. In this way, the scale-out GPU compute (1107D) layer mayoffload to the scale-out storage (1107F) layer some of the computationaltasks that would otherwise be performed by the scale-out GPU compute(1107D) layer.

In some examples, the scale-out storage (1107F) layer may manage acompute cluster so that data analysis and/or data transformation happenunder a centralized management plane. In other examples, the scale-outstorage (1107F) layer may initiate data analysis and/or datatransformation or data management operation without any instruction orcommand from the scale-out GPU compute (1107D) layer, where theinitiation of a data analysis and/or data transformation, or datamanagement operation may be based at least in part on the one or morecontrollers identifying a pattern within the operations requested fromthe scale-out GPU compute (1107D) layer via the API. In some examples, agiven GPU within the scale-out GPU compute (1107D) layer may communicatedirectly with a storage device of the scale-out storage (1107F) layerwithout the intervention of an operating system.

In some implementations, the scale-out GPU compute (1107D) layer maymake calls to the API of the scale-out files/objects protocols (1107E)layer or the scale-out GPU compute (1107D) layer may make calls directlyto the scale-out storage (1107F) layer.

Similarly, the scale-out storage (1107F) layer may generate resultsdirectly to the system memory of one or more GPUs within the scale-outGPU compute (1107D) layer. For example, the scale-out storage (1107E)layer may write results from an API call directly into a cache or othermemory component of one or more GPUs of the scale-out GPU compute(1107D) layer.

As depicted in FIG. 11D, the example method includes generating (1152),at a graphical processing unit of a computer system, a function call(1152A) specifying one or more operations to be performed by a storagesystem of the computer system; transmitting (1154), across acommunication fabric of the computer system, the function call (1152A)from the graphical processing unit to the storage system (1154);generating (1156), at the storage system of the computer system andbased on the function call (1152A), one or more results (1156A); andtransmitting (1158), across the communication fabric, the one or moreresults (1156A) from the storage system to the graphical processingunit.

In this example, the graphical processing unit may be any of thegraphical processing units of the GPU system(s) 1130A-1130N, thecomputer system may be a computer system comprising the artificialintelligence and machine learning infrastructure (1100), and the storagesystem may be any storage system of the storage systems of storagesystem(s) (1120). Further, in this example, the artificial intelligenceand machine learning infrastructure system (1100) may be operating toperform one or more machine learning tasks received from a cloud AIservice (1171) implemented as a cloud service within a cloud servicesprovider (1173A, where the cloud AI service (1171) receives tasks from ahost computer (1170) across a network (not depicted), where the tasksmay be specified via a user interface provided by the cloud AI service(1171). Further, the artificial intelligence and machine learninginfrastructure system (1100) may be implemented within a data center(not depicted) or on site at a client location.

Generating (1152), 1152), at the graphical processing unit of thecomputer system, the function call (1152A) specifying one or moreoperations to be performed by a storage system of the computer systemmay be implemented as described above with reference to FIGS. 11A-11C,where given a specific task, the GPU identifies a corresponding APIcall, and generates parameters for the API call.

Transmitting (1154), across a communication fabric of the computersystem, the function call (1152A) from the graphical processing unit tothe storage system (1154) may be implemented as described above withreference to FIGS. 11A-11C, where the function call (1152A) istransmitted across a communication port to one a network switch, andwhere the network switch routs the function call to a network port on atthe storage system(s) (1120).

Generating (1156), at the storage system of the computer system andbased on the function call (1152A), one or more results (1156A) may beimplemented as described above with reference to FIGS. 11A-11C, whereone or more controllers on the storage system(s) (1120) may perform thefunction call according to the operation and parameters specified by thefunction call.

Transmitting (1158), across the communication fabric, the one or moreresults (1156A) from the storage system to the graphical processing unitmay be implemented as described above with reference to FIGS. 11A-11C,where the results (1156A) are transmitted across a communication port toa network switch, and where the network switch routs the results (1156A)to a network port on at the GPU system(s) (1130A-1130N).

For further explanation, FIG. 12A sets forth a flow chart illustratingan example method of monitoring an artificial intelligence and machinelearning infrastructure (1100) according to some embodiments of thepresent disclosure. The artificial intelligence and machine learninginfrastructure (1100) described above may include one or more monitoringmodules (1202 a, 1202 b, 1202 n) or may be otherwise coupled to one ormore monitoring modules. The monitoring modules (1202 a, 1202 b, 1202 n)may be embodied, for example, computer program instructions executing oncomputer hardware such as a CPU. Such computer program instructions maybe stored, for example, within memory that is contained in one or moreof the blades that is included within a storage system that is includedwithin the artificial intelligence and machine learning infrastructure(1100) and executed by one or more CPUs that are included within thestorage system that is included within the artificial intelligence andmachine learning infrastructure (1100). Readers will appreciate thatother embodiments are contemplated such as, for example, the one or moremonitoring modules (1202 a, 1202 b, 1202 n) residing within and beingexecuted by a server that is included within the artificial intelligenceand machine learning infrastructure (1100), the one or more monitoringmodules (1202 a, 1202 b, 1202 n) residing within and being executed bycloud computing resources that the artificial intelligence and machinelearning infrastructure (1100) is in communications with, or in someother way.

The example method depicted in FIG. 12A includes identifying (1203), bythe one or more monitoring modules (1202 a, 1202 b, 1202 n), abottleneck in an execution pipeline. The execution pipeline may beembodied, for example, as an artificial intelligence or machine learningpipeline in which various stages of executing an artificial intelligenceor machine learning application are carried out. Such an executionpipeline can include, for example, identifying a particular dataset touse as input to the artificial intelligence or machine learningapplication, reading such a dataset from storage that is containedwithin the artificial intelligence and machine learning infrastructure(1100), performing a series of transformations to the dataset, runningthe dataset through a plurality of artificial intelligence or machinelearning models, retaining auditing information describing the stepsperformed and the content of the dataset during the various stages ofexecution, and many other steps.

In the example method depicted in FIG. 12A, a bottleneck can occur for avariety of reasons. For example, a bottleneck can occur wheninsufficient resources are allocated to one portion of the executionpipeline, thereby causing one portion of the execution pipeline tocreate a bottleneck for the remaining portions of the executionpipeline. Consider an example in which one portion of the executionpipeline includes a series of transformations to the dataset, where eachtransformation in the series of transformations is performed by adistinct module of computer program instructions. In such an example,assume that when a first module of computer program instructions hascompleted a first transformation, the first module of computer programinstructions sends the transformed data to a second module of computerprogram instructions which will perform a second transformation. Furtherassume that when the second module of computer program instructions hascompleted the second transformation, the second module of computerprogram instructions sends the transformed data to a third module ofcomputer program instructions which will perform a third transformation.In such an example, assume that the second transformation is morecomplex than the other transformations and further assume that eachmodule of computer program instructions is given an identical amount ofprocessing resources upon which the modules will execute. In such anexample, the performance of the second transformation could create abottleneck as the second transformation may take more time to completegiven that it is the most complex transformation and further given thatthe second module of computer program instructions only has access tothe same amount of computing resources as the first module of computerprogram instructions and the third module of computer programinstructions.

The example method depicted in FIG. 12A also includes initiating (1204),by the one or more monitoring modules (1202 a, 1202 b, 1202 n),reconfiguration of the artificial intelligence and machine learninginfrastructure (1100) to resolve the bottleneck in the executionpipeline. Initiating, by the one or more monitoring modules (1202 a,1202 b, 1202 n), reconfiguration of the artificial intelligence andmachine learning infrastructure (1100) to resolve the bottleneck in theexecution pipeline may be carried out, for example, by reallocatingresources to resolve the bottleneck in the execution pipeline.Continuing with the example described above, initiating reconfigurationof the artificial intelligence and machine learning infrastructure(1100) to resolve the bottleneck in the execution pipeline may becarried out, for example, by the one or more monitoring modules (1202 a,1202 b, 1202 n) allocating additional compute resources to support theexecution of the second module of computer program instructions. Readerswill appreciate that the example described above is just one of manybottlenecks that can occur and the actions taken to resolve suchbottlenecks can take many other forms. For example, bottlenecks mayoccur as the result of processing bottlenecks, scheduling bottlenecks,workload allocation and distribution bottlenecks, and many others. Assuch, the actions taken to resolve such bottlenecks can includesplitting a single step into multiple steps and vice versa, changing themanner in which operations are scheduled, moving workloads around todifferent physical or virtual resources, and so on.

The example method depicted in FIG. 12A can also include monitoring(1206) access patterns to one or more of the storage systems containedin the artificial intelligence and machine learning infrastructure(1100). Monitoring (1206) access patterns to one or more of the storagesystems contained in the artificial intelligence and machine learninginfrastructure (1100) may be carried out, for example, by tracking thelocation of accesses to the storage systems, by tracking the types ofaccesses (e.g., reads, writes) to the storage systems, and so on. Insuch an example, the access patterns to one or more of the storagesystems contained in the artificial intelligence and machine learninginfrastructure (1100) may be used to gain certain insights into theexecution of the artificial intelligence or machine learning pipeline.

Consider an example in which a time-series database is being built offof the I/O access patterns of the training data and a time-seriesdatabase is also being built off of the scheduler and the GPUs. In suchan example, this information could be used to determine how to schedulethings in a way to make best use of the artificial intelligence andmachine learning infrastructure's (1100) resources. In such an example,the artificial intelligence or machine learning pipeline may berepresented by a complicated execution graph and a scheduler must decidewhat to run when. In such an example, feedback loops from storage,networking, compute, and any other parts of the system stack may be usedto inform the scheduler and enable the scheduler to make betterscheduling decisions. In fact, all of this information could bemaintained in a centralized time-series database that includes all ofthis information. As such, information from a first training run can beused to make better decisions on a second training run. Readers willappreciate that although depicted as a distinct step, in someembodiments, monitoring (1206) access patterns to one or more of thestorage systems contained in the artificial intelligence and machinelearning infrastructure (1100) may be part of identifying (1203) abottleneck in an execution pipeline, as described above.

The example method depicted in FIG. 12A also includes monitoring (1208)data-related aspects of the artificial intelligence or machine learningpipeline. Monitoring (1208) data-related aspects of the artificialintelligence or machine learning pipeline can include not onlymonitoring whether some data that is needed by one or more of the GPUsis available for use by the GPUs, but also monitoring the nature of thedata. For example, during each training run of a particular AI ormachine learning model, data may be ingested as training data for the AIor machine learning model. In such an example, monitoring the nature ofthe data can include, for example, monitoring the training data that isingested during each training run to identify exceptional data (i.e.,data that is dissimilar to data that was previously received trainingdata for the AI or machine learning model). In such an example, bymonitoring (1208) data-related aspects of the artificial intelligence ormachine learning pipeline, changes to the input data to the artificialintelligence or machine learning pipeline can be identified. Readerswill appreciate that while the previous sentences relate to themonitoring of training data, in a production environment, data-relatedaspects of the artificial intelligence or machine learning pipeline maysimilarly be monitored (1208).

The example method depicted in FIG. 12A also includes creating (1210)auditing information for the artificial intelligence or machine learningpipeline. The auditing information for the artificial intelligence ormachine learning pipeline may include, for example, informationdescribing the data that was fed into the artificial intelligence ormachine learning pipeline, the source code that was used when executingthe artificial intelligence or machine learning pipeline, and so on.Consider an example in which the pipeline is an artificial intelligencepipeline for a self-driving car. In such an example, auditinginformation may be maintained to capture what data was fed into theartificial intelligence pipeline (e.g., what data was received from theself-driving car's sensors at various points in time), what code wasexecuted to control the operation of the self-driving car, and so on.The auditing information may be creating, for example, by applying ahash function to representations of the data and code to create a hashvalue that captures the data and code, by storing such information in ablockchain, by storing such information in a database, and so on.

Readers will appreciate that creating (1210) auditing information forthe artificial intelligence or machine learning pipeline may also takeadvantage of an approach to only retain the deltas each time auditinginformation is created. For example, if auditing information is createdat time 0 and auditing information is subsequently created at time 1,any audit information that has not changed between time 1 and time 0 maynot need to be retained. For example, if the code that was used at time0 is captured in the auditing information for time 0, and such code doesnot change at time 1, then the code that was used at time 1 need not beincluded in the auditing information for time 1. In such an example, apointer or other instrument can be included in the auditing informationfor time 1 to indicate that the code used at time 1 was identical to thecode used at a previous point in time.

The example method depicted in FIG. 12A also includes creating (1212)trending information for the artificial intelligence or machine learningpipeline. The trending information for the artificial intelligence ormachine learning pipeline may include, for example, informationdescribing improvements in the models over time, information describingchanges to the data that is input into the models over time, and so on.In such an example, the trending information for the artificialintelligence or machine learning pipeline may be used to validatecertain models, identify data drift, or used for a variety of otherpurposes. In such an example, the trending information for theartificial intelligence or machine learning pipeline may be displayedand presented to a user, for example, via a tool that shows theimprovement of a particular model over time.

Readers will appreciate that although the embodiment depicted in FIG.12A illustrates an embodiment where the one or more monitoring modules(1202 a, 1202 b, 1202 n) reside within the artificial intelligence andmachine learning infrastructure (1100), other embodiments can exist. Infact, in an alternative embodiment the one or more monitoring modules(1202 a, 1202 b, 1202 n) may reside outside of the artificialintelligence and machine learning infrastructure (1100). The one or moremonitoring modules (1202 a, 1202 b, 1202 n) may reside, for example, onone or more remote servers that communicate with one or more artificialintelligence and machine learning infrastructures (1100). Alternatively,the one or more monitoring modules (1202 a, 1202 b, 1202 n) may residewithin a cloud environment that includes resources that can communicatewith one or more artificial intelligence and machine learninginfrastructures (1100). In such embodiments, the one or more artificialintelligence and machine learning infrastructures (1100) mayperiodically send telemetry data to the one or more monitoring modules(1202 a, 1202 b, 1202 n) that includes, for example, data telemetry,storage telemetry, networking telemetry, compute telemetry, and so on.

For further explanation, FIG. 12B sets forth a flow chart illustratingan example method of optimizing an artificial intelligence and machinelearning infrastructure (1100) according to some embodiments of thepresent disclosure. The artificial intelligence and machine learninginfrastructure (1100) described above may include one or moreoptimization modules (1252 a, 1252 b, 1252 n) or may be otherwisecoupled to one or more optimization modules. The optimization modules(1252 a, 1252 b, 1252 n) may be embodied, for example, computer programinstructions executing on computer hardware such as a CPU. Such computerprogram instructions may be stored, for example, within memory that iscontained in one or more of the blades that is included within a storagesystem that is included within the artificial intelligence and machinelearning infrastructure (1100) and executed by one or more CPUs that areincluded within the storage system that is included within theartificial intelligence and machine learning infrastructure (1100).Readers will appreciate that other embodiments are contemplated such as,for example, the one or more optimization modules (1252 a, 1252 b, 1252n) residing within and being executed by a server that is includedwithin the artificial intelligence and machine learning infrastructure(1100), the one or more optimization modules (1252 a, 1252 b, 1252 n)residing within and being executed by cloud computing resources that theartificial intelligence and machine learning infrastructure (1100) is incommunications with, or in some other way.

The example method depicted in FIG. 12B includes determining (1254)whether a particular artificial intelligence or machine learningpipeline will fit on a particular artificial intelligence and machinelearning infrastructure (1100). Readers will appreciate that multipleartificial intelligence or machine learning pipelines may be executed ona particular artificial intelligence and machine learning infrastructure(1100). Each artificial intelligence or machine learning pipeline thatis being executed on a particular artificial intelligence and machinelearning infrastructure (1100) will consume resources (e.g., storage,compute, networking). Given that each artificial intelligence andmachine learning infrastructure (1100) has finite resources, eachartificial intelligence and machine learning infrastructure (1100)cannot support an infinite number of artificial intelligence or machinelearning pipelines. As such, a determination (1254) may need to be madeas to whether a particular artificial intelligence or machine learningpipeline will fit on a particular artificial intelligence and machinelearning infrastructure (1100). Determining (1254) whether a particularartificial intelligence or machine learning pipeline will fit on aparticular artificial intelligence and machine learning infrastructure(1100) may be carried out, for example, by determining an amount ofresources that are expected to be required to execute a particularartificial intelligence or machine learning pipeline and determiningwhether the artificial intelligence and machine learning infrastructure(1100) has an amount of available resources to satisfy the expecteddemand for resources from the particular artificial intelligence ormachine learning pipeline.

Readers will appreciate that determining (1254) whether a particularartificial intelligence or machine learning pipeline will fit on aparticular artificial intelligence and machine learning infrastructure(1100) can be more complicated than a simple comparison of availableresources to expected demand for resources by the particular artificialintelligence or machine learning pipeline. For example, the optimizationmodules (1252 a, 1252 b, 1252 n) may take into consideration theperformance impact on other artificial intelligence or machine learningpipelines that are currently executing on the particular artificialintelligence and machine learning infrastructure (1100) to determinewhether satisfactory performance metrics could be maintained even withthe addition of the particular artificial intelligence or machinelearning pipeline to the particular artificial intelligence and machinelearning infrastructure (1100). In such an example, other artificialintelligence or machine learning pipelines that are currently executingon the particular artificial intelligence and machine learninginfrastructure (1100) may be subject to various service levelagreements, quality of service requirements, and so on that may beviolated with the addition of the particular artificial intelligence ormachine learning pipeline to the particular artificial intelligence andmachine learning infrastructure (1100)—even if the particular artificialintelligence and machine learning infrastructure (1100) couldtechnically support the particular artificial intelligence or machinelearning pipeline. Likewise, the particular artificial intelligence ormachine learning pipeline may itself have various performance andservice requirements/expectations that are attached to the particularartificial intelligence or machine learning pipeline, such that the mereability to support the execution of the particular artificialintelligence or machine learning pipeline may be insufficient.

Readers will further appreciate that trending information, including theexpected increase or decrease in resource consumption of the particularartificial intelligence or machine learning pipeline, as well as theexpected increase or decrease in resource consumption of the otherartificial intelligence or machine learning pipelines that are currentlyexecuting on the particular artificial intelligence and machine learninginfrastructure (1100) may be taken into consideration when determining(1254) whether a particular artificial intelligence or machine learningpipeline will fit on a particular artificial intelligence and machinelearning infrastructure (1100). In such a way, the determination (1254)may be forward looking and avoid a predictable exhaustion of resources.

Readers will further appreciate that determining (1254) whether aparticular artificial intelligence or machine learning pipeline will fiton a particular artificial intelligence and machine learninginfrastructure (1100) may be of particular interest in embodiments wherea cluster of artificial intelligence and machine learninginfrastructures (1100) are available. In such an example, although aplurality of the artificial intelligence and machine learninginfrastructures (1100) may be able to support the execution of theparticular artificial intelligence or machine learning pipeline, a bestfit analysis may be performed to identify the artificial intelligenceand machine learning infrastructures (1100) that may best support theparticular artificial intelligence or machine learning pipeline. In sucha way, loading balancing objectives may be met, higher service levelsmay be afforded to the other artificial intelligence or machine learningpipelines that are currently executing on the cluster of artificialintelligence and machine learning infrastructures (1100), and so on.

The example method depicted in FIG. 12B also includes, responsive toaffirmatively determining that the particular artificial intelligence ormachine learning pipeline will fit on the particular artificialintelligence and machine learning infrastructure (1100), initiating(1256) execution of the particular artificial intelligence or machinelearning pipeline on the particular artificial intelligence and machinelearning infrastructure (1100). Readers appreciate that in embodimentswhere a cluster of artificial intelligence and machine learninginfrastructures (1100) are available, execution of the particularartificial intelligence or machine learning pipeline may be initiated(1256) on a particular artificial intelligence and machine learninginfrastructure (1100) that was selected using a best fit analysis.

The example method depicted in FIG. 12B also includes determining (1258)an estimated time for completion for a particular artificialintelligence or machine learning job. Determining (1258) an estimatedtime for completion for a particular artificial intelligence or machinelearning job may be carried out, for example, by estimating an amount oftime required to complete a particular artificial intelligence ormachine learning job in view of the amount of resources that may be madeavailable for use by the particular artificial intelligence or machinelearning job. In such an example, users in a multi-tenant environmentmay even be provided with the estimated time for completion for aparticular artificial intelligence or machine learning job, so that auser may determine whether to actually submit the particular artificialintelligence or machine learning job. Likewise, the estimated time forcompletion for a particular artificial intelligence or machine learningjob may be given to a scheduler or other module of computer programinstructions that can gather such information from a plurality ofartificial intelligence and machine learning infrastructures (1100)(e.g., in a clustered environment) in order to identify which particularartificial intelligence and machine learning infrastructure (1100) theparticular artificial intelligence or machine learning job should besubmitted to.

The example method depicted in FIG. 12B also includes determining (1260)the extent to which one or more artificial intelligence or machinelearning models are improving over time. Determining (1260) the extentto which one or more artificial intelligence or machine learning modelsare improving over time may be carried out, for example, through the useof trending information for a particular artificial intelligence ormachine learning job. In fact, determining (1260) the extent to whichone or more artificial intelligence or machine learning models areimproving over time can include performing things like A/B testingbetween different models or transformations, performing canary testingto quickly and automatically verify that everything that a particularmodel depends on is ready before other time-consuming tests areconducted, and so on. In fact, in context of canary testing, a deeplylearned model may be used that predicts if the learned model passed A/Btesting using a history of previous A/B tests, particular for acontinuous integration pipeline. In such an example, weighted scores maybe created to show if the output is likely to pass. Through the use ofsuch techniques, historical trending of various models may be maintainedand tracked such that the details and outcomes of steps in a pipelinemay be maintained.

The example method depicted in FIG. 12B also includes generating (1262)model recommendations. Readers will appreciate that, in view of the factthat many artificial intelligence or machine learning pipelines may beexecuted a single artificial intelligence and machine learninginfrastructure (1100) and further in view of the fact that multipleartificial intelligence and machine learning infrastructures (1100) maybe included in a single cluster, a substantial amount of informationrelated to the execution of artificial intelligence or machine learningpipelines may be available. Such information may be mined to identify,for example, models that worked well on various datasets,transformations that led to improvements for a particular pipeline anddataset, and so on. As such, model recommendations may be generated(1262) to recommend that a particular model be alerted in someparticular way, particular transformations be excluded from or includedin a particular, transformations be modified in some way, and so on.

In the example method depicted in FIG. 12B, generating (1262) modelrecommendations may be carried out through the fingerprints or similarmechanisms that describe various aspects of a particular artificialintelligence or machine learning pipeline, the data ingested by theparticular artificial intelligence or machine learning pipeline, and soon. In such a way, recommendations may only be generated based oninformation gathered from artificial intelligence or machine learningpipelines and datasets with similar fingerprints. For example, if aparticular transformation was particularly useful in an imagerecognition machine learning pipeline that ingested images with certaincharacteristics, such a transformation may only be recommended forowners of other image recognition machine learning pipelines that ingestimages with similar characteristics, whereas such a recommendation wouldnot be generated a speech processing artificial intelligence pipeline.Readers will appreciate that such recommendations could be anonymized soas to shield another user's data, specific information about theirmodel, and so on.

In the example method depicted in FIG. 12B, embodiments may make use ofauto-indexing techniques through which the artificial intelligence andmachine learning infrastructure (1100) can, for example, generatevectors for data to quickly and effectively index and understand largeamounts of data. Such auto-indexing techniques may be used to identifycold data that should be tiered off of the artificial intelligence andmachine learning infrastructure (1100), to migrate data to a cache(e.g., for data that is being heavily used), and so on. Through the useof such auto-indexing techniques, insights into the content of the datamay cause the artificial intelligence and machine learninginfrastructure (1100) to automatically tier some less useful data toslower storage as part of a migration process, rather than migrating thedata and subsequently determining that the data that has already beenstored in the artificial intelligence and machine learninginfrastructure (1100) should be tiered away.

The example method depicted in FIG. 12B also includes tuning (1212) anartificial intelligence or machine learning pipeline. In the examplemethod depicted in FIG. 12B, tuning (1212) an artificial intelligence ormachine learning pipeline may be carried out, for example, in a mannerthat is automated and/or predictive based on an examination of theworkloads placed on the artificial intelligence and machine learninginfrastructure (1100) as well as the attributes of one or moreartificial intelligence or machine learning pipelines. For example, theratios of compute-to-storage may be modified based on characteristics ofthe workload, pipelines could be rebalanced based on an identificationof bottlenecks (e.g., a bottleneck is identified, a solution isidentified indicating that additional stream-processing servers areneeded, and additional stream-processing servers are automatically spunup). Likewise, workloads or pipelines could be moved around and variousother actions could be taken to tune (1212) the artificial intelligenceor machine learning pipeline.

Embodiments of the artificial intelligence and machine learninginfrastructure (1100) may also make use of a job scheduler and aresource management tool that can reside within the storage system(s)that are contained in the artificial intelligence and machine learninginfrastructure (1100). In such an embodiment, the storage system(s) maybe responsible for managing the scheduling of jobs to the GPU and othertypes of resource management, where such management is carried out bythe storage system(s) under a single management plane. Furthermore, suchmanagement may be carried out in an automated fashion, includingautomated scheduling based on various factors (e.g., the influx of somedata, data contents, and so on). For example, pre-merge tests should seewhat code has changed and run tests based on those changes. Furthermore,the storage systems(s) could implement management in by making decisionssuch as, for example, selecting a particular dataset to train against,the appropriate interval to run tests and continuously re-train with newdata, and so on.

In some embodiments, a storage system or other management entity withinthe artificial intelligence and machine learning infrastructure (1100)may also implement automated training with continuous learning based onsome triggers (e.g., new data, exceptional data). Furthermore,auto-indexing could be used to identify the particular categories ofdata within a dataset. For example, a user of an image processingpipeline may want to train against images of dogs and cats, with nounderstanding the dataset actually includes images of dogs, cats, birds,worms, and so on. An automated indexing solution, however, would detecteach of the categories of data actually contained within the dataset.

In some embodiments, a storage system or other management entity withinthe artificial intelligence and machine learning infrastructure (1100)may also implement the real-time coordination of workflows. Readers willappreciate that the artificial intelligence and machine learninginfrastructure (1100) do not just execute artificial intelligence andmachine learning pipelines, as the artificial intelligence and machinelearning infrastructure (1100) may also run message queue systems, datacleansing modules, and so on. As such, the artificial intelligence andmachine learning infrastructure (1100) may be configured to handle thecoordination of all of the resources under a single management plane.

For further explanation, FIG. 13 sets forth a flow chart illustrating anexample method of data transformation caching in an artificialintelligence infrastructure (1302) that includes one or more storagesystems (1304) and one or more GPU servers (1318) according to someembodiments of the present disclosure. Although depicted in less detail,the storage system (1304) depicted in FIG. 13 may be similar to thestorage systems described above, as the storage system (1304) depictedin FIG. 13 may include any combination of the components contained inthe storage systems described above. The GPU servers (1318) depicted inFIG. 13 may be embodied, for example, as a server, workstation, or othercomputing device that specialize in using general-purpose computing ongraphics processing units (‘GPGPU’) to accelerate deep learningapplications, machine learning applications, artificial intelligenceapplications, or similar applications. Although not explicitly depictedin FIG. 13, the storage systems (1304) and the GPU servers (1318) may becoupled for data communications via one or more data communicationslinks. Readers will appreciate that the artificial intelligenceinfrastructure (1302) depicted in FIG. 13 may be similar to theartificial intelligence and machine learning infrastructures describedabove.

The artificial intelligence infrastructure (1302) depicted in FIG. 13may be configured to support the execution of one or more machinelearning models. Such machine learning models may consist of one or moremachine learning algorithms that are executed on one or more of the GPUservers (1308). Such machine learning algorithms can include supervisedlearning algorithms such as, for example, linear regression algorithms,logistic regression algorithms, decision tree algorithms, or others.Such machine learning algorithms can also include unsupervised learningalgorithms such as, for example, Apriori algorithms, k-means clusteringalgorithms, or others. Likewise, such machine learning algorithms canalso include reinforcement learning algorithms such as, for example,Markov decision processes, Q-learning algorithms, or others.

In the examples depicted herein, the machine learning models that aresupported by the artificial intelligence infrastructure (1302) may beprovided input data that is stored within one or more of the storagesystems (1304) that are included in the artificial intelligenceinfrastructure (1302). As such, input data that is stored within one ormore of the storage systems (1304) that are included in the artificialintelligence infrastructure (1302) may be provided to the GPU servers(1308) such that the GPU servers (1308) can utilize the input data asinput into the machine learning algorithms that are being executed onthe GPU servers (1308). Readers will appreciate, however, that differentmachine learning models may require input data that is in differentformats, contains different types of data, and so on. For example, afirst machine learning model may utilize a vector as input while asecond machine learning model may utilize a matrix as input.

The example method depicted in FIG. 13 includes identifying (1308), independence upon one or more machine learning models (1316) to beexecuted on the GPU servers (1318), one or more transformations to applyto a dataset (1306). The dataset (1306) depicted in FIG. 13 may beembodied, for example, as a collection of files, objects, or otherpieces of data that collectively form a set of data that is to be usedfor training a machine learning model. The dataset (1306) depicted inFIG. 13 may, however, not be in a format that can be efficiently used bya machine learning model. For example, the objects in the dataset (1306)may contain unstructured data that either does not have a pre-defineddata model or is not organized in a pre-defined manner. Suchunstructured data may be, for example, text-heavy data that containsdata such as dates, numbers, and facts as well. Such unstructured datamay be difficult to understand using traditional programs relative todata stored in fielded form in databases, annotated in documents, orotherwise structured. Alternatively, the objects in the dataset (1306)may contain untagged data whose meaning cannot be readily identified bya machine learning model. Readers will appreciate that in otherexamples, the contents of the dataset (1306) may be inefficientlyformatted, tagged, or otherwise inefficient for use as training data fora machine learning model.

Consider an example in which the dataset (1306) is embodied as acollection of log files generated by the storage system (1304). In suchan example, each line in each of the log files may be unstructured aseach line is created in a way so as to be in a human readable format.Such unstructured data may be inefficient for use by a machine learningmodel as the unstructured data may not be structured via pre-defineddata models or schema that enable for easy searching of the data. Otherexamples of datasets (1306) that contain unstructured data can include,for example, datasets that include video files, image files, audiofiles, and many others.

In the example method depicted in FIG. 13, the one or moretransformations to apply to the dataset (1306) may include, for example,performing scaling transformations to standardize the range ofindependent variables or features of data, performing decompositiontransformations to decompose features that represent a complex conceptinto constituent parts (e.g., decomposing a date that has day and timecomponents an hour of the day constituent part), performing aggregationtransformations to aggregate multiple features into a single feature(e.g., instances for each time a customer logged into a system could beaggregated into a count feature that identifies the number of logins),and many others. Readers will appreciate that the specifictransformations to apply to the dataset (1306) may not only be afunction of the format of the dataset (1306) itself, but specifictransformations to apply may also be a function of the expected inputfor the one or more machine learning models (1316) to be executed on theGPU servers (1318). The one or more transformations to apply to thedataset (1306) can further include, for example, transformingunstructured data into structure data by extracting information from theunstructured format and populating the data in a structured format,transforming structured data in a first format to a second format thatis expected by the one or more machine learning models (1316), and soon.

The example method depicted in FIG. 13 also includes generating (1310),in dependence upon the one or more transformations, a transformeddataset (1304). The transformed dataset (1314) may be embodied, forexample, as vector that can serve as input to a machine learning model,as a tensor that can serve as an input to a machine learning model, andso on. FIG. 13 relates to an embodiment where something other than thestorage system (1304) generates (1310) the transformed dataset (1304) independence upon the one or more transformations. For example, theartificial intelligence infrastructure (1302) may include othercomputing devices (e.g., dedicated servers) that generate (1310) thetransformed dataset (1304). Likewise, in other embodiments the GPUservers (1318) may be used to generate (1310) the transformed dataset(1304). In additional embodiments, generating (1310) the transformeddataset (1304) may be offloaded to a cloud services provider that is indata communications with the artificial intelligence infrastructure(1302). Readers will appreciate that prior to actually generating (1310)the transformed dataset (1314), the storage system (1304) or othercomputing resources that are performing the transformation may performother operations to prepare the dataset (1306) for use by the machinelearning models that are supported by the artificial intelligenceinfrastructure (1302). For example, the storage system (1304) or othercomputing resources that are performing the transformation may selectdata for inclusion in the transformed dataset (1314), format the data toensure that data formats are consistent for data received from differentsources, clean the data to discard unwanted data, remove duplicateddata, delete unusable data, handle missing data, or perform otherpreprocessing operations.

Readers will appreciate that in embodiments where the storage system(1304) or other computing resources that performs the steps describedabove, the GPU servers (1318) that actually execute the machine learningalgorithms may avoid performing the computationally demanding task ofpreparing data for use by the machine learning algorithms, as theprocess of receiving, cleaning, pre-processing, and transforming thedata may be performed by the storage system (1304) rather than the GPUservers (1318). As such, the computing resources provided by the GPUservers (1318) may be reserved for actually executing the machinelearning algorithms against an already prepared transformed dataset(1314), rather than having the computing resources provided by the GPUservers (1318) burdened with the task of preparing data for ingestion bythe machine learning algorithms.

The example method depicted in FIG. 13 also includes storing (1312),within one or more of the storage systems (1304), the transformeddataset (1314). In the example method depicted in FIG. 13, portions ofthe transformed dataset (1314) may be stored across multiple storagedevices within the storage system (1304), along with parity data, toincrease the resiliency of the transformed dataset (1314) through theuse of a RAID (e.g., RAID 6) or RAID-like approach. Furthermore,concepts such as, for example, data tiering may be applied when storing(1312) the transformed dataset (1314) such that more frequently accessedtransformed datasets are stored in portions of the storage system (1304)that provide for faster access while less frequently accessedtransformed datasets are stored in portions of the storage system (1304)that provide for slower access. In fact, such concepts can be extendedsuch that transformed datasets are tiered away from the storage systems(1304) themselves and stored on, for example, storage that is providedby a cloud services provider. In such examples, heuristics may be usedto place and move the transformed datasets within a storage environmentthat can include the one or more storage systems (1304) as well asstorage resources that may exist outside of the artificial intelligenceinfrastructure (1302), although in other embodiments storing (1312) thetransformed dataset (1314) occurs exclusively within one or more of thestorage systems (1304) that reside within the artificial intelligenceinfrastructure (1302).

The example method depicted in FIG. 13 also includes receiving (1320) aplurality of requests (1324) to transmit the transformed dataset (1314)to one or more of the GPU servers (1318). Readers will appreciate thatmultiple requests (1324) for the same transformed dataset (1314) may bereceived (1320) for a variety of reasons. For example, a first requestto transmit the transformed dataset (1314) to one or more of the GPUservers (1318) may be received (1320) in response to the GPU servers(1318) initiating execution of a particular machine learning model thatwill train on the transformed dataset (1314). In such an example, aftertraining has completed, changes may be made to the particular machinelearning model as part of an effort to improve the particular machinelearning model. Once the changes have been made to the particularmachine learning model, a second request to transmit the transformeddataset (1314) to one or more of the GPU servers (1318) may be received(1324) in response to the GPU servers (1318) initiating execution of theupdated machine learning model that will train on the transformeddataset (1314). Similarly, multiple requests for the same transformeddataset (1314) may be received (1320), for example, when a first GPUserver is going to execute a first version of a particular machinelearning model that trains on the transformed dataset (1314) more orless simultaneously to a second GPU server executing a second version ofa particular machine learning model that trains on the transformeddataset (1314). Readers will appreciate that, because the one or morestorage systems (1304) can store the transformed dataset (1314) withinthe storage systems (1304) themselves, neither the storage systems(1304) nor the GPU servers (1318) will need to repeat a transformationthat has previously been performed.

The example method depicted in FIG. 13 also includes, responsive to eachrequest (1324), transmitting (1322), from the one or more storagesystems (1304) to the one or more GPU servers (1318) withoutre-performing the one or more transformations on the dataset (1306), thetransformed dataset (1314). The transformed dataset (1314) may betransmitted (1322) from the storage system (1304) to the one or more GPUservers (1318), for example, via one or more data communications linksbetween the storage system (1304) and the one or more GPU servers(1318), which may be embodied in many different ways as described inmore detail above. Transmitting (1322) the transformed dataset (1314)from the storage system (1304) to the one or more GPU servers (1318) maybe carried out, for example, via RDMA. Transmitting (1322) thetransformed dataset (1314) via RDMA may be carried out, for example, bya network adapter that is included in the storage system (1304)transferring the transformed dataset (1314) directly from memory in thestorage system (1304) to memory within the one or more GPU servers(1318). Through the use of such an RDMA transfer, the operating systemand the GPUs within the GPU servers (1318) may be bypassed such that nowork is required by the GPUs within the GPU servers (1318) to obtain thetransformed dataset (1314), as would be required in non-RDMA transfers(e.g., message-based transfers) were used. Readers will appreciate thatthe use of RDMA transfers is an additional mechanism that can enable theGPU servers (1318) that actually execute the machine learning algorithmsto avoid performing the computationally demanding task of obtaining thetransformed dataset (1314). As such, the computing resources provided bythe GPU servers (1318) may be reserved for actually executing themachine learning algorithms against an already prepared transformeddataset (1314), rather than having the computing resources provided bythe GPU servers (1318) burdened with the task of obtaining thetransformed dataset (1314). In such a way, the one or more storagesystems (1304) may effectively operate as a cache that can be used bythe GPU servers (1318) to obtain already transformed datasets (1314).

For further explanation, FIG. 14 sets forth a flow chart illustrating anadditional example method of data transformation caching in anartificial intelligence infrastructure (1302) that includes one or morestorage systems (1304) and one or more GPU servers (1318) according tosome embodiments of the present disclosure. The example method depictedin FIG. 14 is similar to the example method depicted in FIG. 13, as theexample method depicted in FIG. 14 also includes identifying (1308) oneor more transformations to apply to a dataset (1306), generating (1310)a transformed dataset (1304), storing (1312) the transformed dataset(1314) within one or more of the storage systems (1304), receiving(1320) a plurality of requests (1324) to transmit the transformeddataset (1314) to one or more of the GPU servers (1318), and responsiveto each request (1324), transmitting (1322) the transformed dataset(1314) from the one or more storage systems (1304) to the one or moreGPU servers (1318) without re-performing the one or more transformationson the dataset (1306).

In the example method depicted in FIG. 14, the storage system (1304)both identifies (1308) one or more transformations to apply to thedataset (1306) and generates (1310) the transformed dataset (1304).Readers will appreciate that, as described above, the storage system(1304) may include a variety of computing resources to perform suchtasks. As such, the storage systems (1304) may be configured to includecomputer program instructions that, when executed by the computingresources within the storage system (1304), perform the steps ofidentifying (1308) one or more transformations to apply to the dataset(1306) and generating (1310) the transformed dataset (1304).

In the example method depicted in FIG. 14, transmitting (1322) thetransformed dataset (1314) from the one or more storage systems (1304)to the one or more GPU servers (1318) without re-performing the one ormore transformations on the dataset (1306) can include transmitting(1402) the transformed dataset (1314) from the one or more storagesystems (1304) directly to application memory on the GPU servers (1318).Transmitting (1322) the transformed dataset (1314) from the one or morestorage systems directly to application memory on the GPU servers (1318)may be carried, for example, by transmitting the transformed dataset(1314) from the storage system (1304) to the GPU servers (1318) viaRDMA. Transmitting the transformed dataset (1314) via RDMA may becarried out, for example, by a network adapter that is included in thestorage system (1304) transferring the transformed dataset (1314)directly from memory in the storage system (1304) to application memorywithin the one or more GPU servers (1318). Through the use of such anRDMA transfer, the operating system and the GPUs within the GPU servers(1318) may be bypassed such that no work is required by the GPUs withinthe GPU servers (1318) to obtain the transformed dataset (1314), aswould be required in non-RDMA transfers (e.g., message-based transfers)were used. Readers will appreciate that the use of RDMA transfers is anadditional mechanism that can enable the GPU servers (1318) thatactually execute the machine learning algorithms to avoid performing thecomputationally demanding task of obtaining the transformed dataset(1314). As such, the computing resources provided by the GPU servers(1318) may be reserved for actually executing the machine learningalgorithms against an already prepared transformed dataset (1314),rather than having the computing resources provided by the GPU servers(1318) burdened with the task of obtaining the transformed dataset(1314). Readers will appreciate that in other embodiments, transmitting(1322) the transformed dataset (1314) from the one or more storagesystems directly to application memory on the GPU servers (1318) may becarried, for example, through the use of NFS or other appropriatetechnology.

For further explanation, FIG. 15 sets forth a flow chart illustrating anadditional example method of data transformation caching in anartificial intelligence infrastructure (1302) that includes one or morestorage systems (1304) and one or more GPU servers (1318) according tosome embodiments of the present disclosure. The example method depictedin FIG. 15 is similar to the example methods depicted in FIG. 13 andFIG. 14, as the example method depicted in FIG. 15 also includesidentifying (1308) one or more transformations to apply to a dataset(1306), generating (1310) a transformed dataset (1304), storing (1312)the transformed dataset (1314) within one or more of the storage systems(1304), receiving (1320) a plurality of requests (1324) to transmit thetransformed dataset (1314) to one or more of the GPU servers (1318), andresponsive to each request (1324), transmitting (1322) the transformeddataset (1314) from the one or more storage systems (1304) to the one ormore GPU servers (1318) without re-performing the one or moretransformations on the dataset (1306).

The example method depicted in FIG. 15 includes executing (1508), by oneor more of the GPU servers (1318), one or more machine learningalgorithms associated with the machine learning model (1316) using thetransformed dataset (1314) as input. Readers will appreciate that theoutput generated by executing (1508) one or more machine learningalgorithms associated with the machine learning model (1316) using thetransformed dataset (1314) as input may vary in dependence upon theparticular machine learning model that is being carried out.

The example method depicted in FIG. 15 also includes scheduling (1504),by a unified management plane (1502), one or more transformations forone or more of the storage systems (1304) to apply to the dataset(1306). The unified management plane (1502) depicted in FIG. 15 may beembodied, for example, as a module of computer program instructionsexecuting on computer hardware such as one or more CPUs. The unifiedmanagement plane (1502) may be configured to monitor and manage allelements within the artificial intelligence infrastructure (1302),including the storage systems (1304), the GPU servers (1318), and anydevices (e.g., network switches) that enable data communications betweenthe storage systems (1304) and the GPU servers (1318). The unifiedmanagement plane (1502) may be configured to perform tasks such as, forexample, scheduling tasks such as one or more dataset transformations tobe performed by one or more of the storage systems (1304), schedulingtasks such as executing of one or more machine learning algorithms onthe one or more GPU servers (1318), managing the amount of storagesystem resources that are made available for performing one or moredataset transformations by one or more of the storage systems (1304),managing the amount of GPU server resources that are made available forexecuting of one or more machine learning algorithms on the one or moreGPU servers (1318), managing data paths between the one or more storagesystems (1304) and the one or more GPU servers (1318), and so on.

Readers will appreciate that, because the unified management plane(1502) has insights into both the storage systems (1304) and the GPUservers (1318) via monitoring both the storage systems (1304) and theGPU servers (1318), the unified management plane (1502) can manage boththe storage systems (1304) and the GPU servers (1318) in a way so as tooptimize interactions between the storage systems (1304) and the GPUservers (1318) and also to optimize the series of steps that are neededto support the execution of a machine learning model. In fact, theunified management plane (1502) may be configured to perform automatedscheduling of tasks on the storage systems (1304) and on the GPU servers(1318) based on various factors (e.g., the influx of some data, datacontents, and so on). For example, the unified management plane (1502)could be configured to decide that a particular machine learning modelshould train against a particular dataset, the unified management plane(1502) could be configured to decide the appropriate interval to runtests and continuously re-train with new data, and so on. In such anexample, the unified management plane (1502) could be configured tosupport automated training with continuous learning based on sometriggers (e.g., new data, exceptional data).

In the example method depicted in FIG. 15, the unified management plane(1502) is configured to schedule (1504) one or more transformations forone or more of the storage systems (1304) to apply to the dataset (1306)and also configured to schedule (1506) execution of one or more machinelearning algorithms associated with the machine learning model (1316) bythe one or more GPU servers (1318). In such an example, the unifiedmanagement plane (1502) may be configured to work with a scheduler onone or more of the storage systems (1304) as well as a scheduler on theone or more GPU servers (1318). The unified management plane (1502) maybe configured to work with a scheduler on one or more of the storagesystems (1304) as well as a scheduler on the one or more GPU servers(1318), for example, by sending one or more messages to the storagesystems (1304) that are understood by the storage system (1304) as ascheduling instruction, by sending one or more messages to the GPUservers (1318) that are understood by the GPU servers (1318) as ascheduling instruction, and so on. In such an example, the storagesystems (1304) and the GPU servers (1318) may be configured, via an APIor some other mechanism, to receive scheduling instructions from theunified management plane (1502) and to implement the schedulinginstructions received from the unified management plane (1502) via oneor more local schedulers.

For further explanation, FIG. 16 sets forth a flow chart illustrating anadditional example method of data transformation caching in anartificial intelligence infrastructure (1302) that includes one or morestorage systems (1304) and one or more GPU servers (1318) according tosome embodiments of the present disclosure. The example method depictedin FIG. 16 is similar to the example methods depicted in FIGS. 13-15, asthe example method depicted in FIG. 16 also includes identifying (1308)one or more transformations to apply to a dataset (1306), generating(1310) a transformed dataset (1304), storing (1312) the transformeddataset (1314) within one or more of the storage systems (1304),receiving (1320) a plurality of requests (1324) to transmit thetransformed dataset (1314) to one or more of the GPU servers (1318), andresponsive to each request (1324), transmitting (1322) the transformeddataset (1314) from the one or more storage systems (1304) to the one ormore GPU servers (1318) without re-performing the one or moretransformations on the dataset (1306).

The example method depicted in FIG. 16 also includes providing (1602),by the unified management plane (1502) to the one or more GPU servers(1318), information (1604) describing the dataset (1306), the one ormore transformations applied to the dataset (1306), and the transformeddataset (1314). The information (1604) describing the dataset (1306),the one or more transformations applied to the dataset (1306), and thetransformed dataset (1314) may be maintained, for example, by an entitysuch as the unified management plane (1502) described above, by thestorage system itself, or by some other component that is within oraccessible to the artificial intelligence infrastructure (1302). Byproviding (1602) the information (1604) describing the dataset (1306),the one or more transformations applied to the dataset (1306), and thetransformed dataset (1314) to the GPU servers (1318), the GPU servers(1318) may be configured to simply request such a transformed dataset(1314) rather than seeking to have the transformations applied again. Assuch, the storage system (1304) may serve as a transformation cache suchthat the computationally intensive process of transforming a dataset(1306) for use by a machine learning model (1316) need not be repeated.Readers will appreciate that, in view of the fact that different machinelearning models may require identical transformations and that differentinstances of the same machine learning mode may require identicaltransformations, by maintaining the information describing the dataset(1306), the one or more transformations applied to the dataset (1306),the transformed dataset (1314), as well as the transformed dataset(1314) itself, the storage system (1304) may serve as a transformationcache whose presence can prevent the GPUs within the GPU servers (1318)from being repeatedly tasked with the computationally intensive processof transforming a dataset (1306) for use by a machine learning model(1316) that is supported by the GPU servers (1318).

Readers will appreciate that although the previous paragraphs relate toembodiments where steps may be described as occurring in a certainorder, no ordering is required unless otherwise stated. In fact, stepsdescribed in the previous paragraphs may occur in any order.Furthermore, although one step may be described in one figure andanother step may be described in another figure, embodiments of thepresent disclosure are not limited to such combinations, as any of thesteps described above may be combined in particular embodiments.

Readers will further appreciate that although the examples describedabove relate to embodiments where an artificial intelligenceinfrastructure supports the execution of machine learning models, theartificial intelligence infrastructure may support the execution of abroader class of AI algorithms, including production algorithms. Infact, the steps described above may similarly apply to such a broaderclass of AI algorithms.

Readers will further appreciate that although the embodiments describedabove relate to embodiments where the artificial intelligenceinfrastructure includes one or more storage systems and one or more GPUservers, in other embodiments, other technologies may be used. Forexample, in some embodiments the GPU servers may be replaced by acollection of GPUs that are embodied in a non-server form factor.Likewise, in some embodiments, the GPU servers may be replaced by someother form of computer hardware that can execute computer programinstructions, where the computer hardware that can execute computerprogram instructions may be embodied in a server form factor or in anon-server form factor.

Example embodiments are described largely in the context of a fullyfunctional computer system. Readers of skill in the art will recognize,however, that the present disclosure also may be embodied in a computerprogram product disposed upon computer readable storage media for usewith any suitable data processing system. Such computer readable storagemedia may be any storage medium for machine-readable information,including magnetic media, optical media, or other suitable media.Examples of such media include magnetic disks in hard drives ordiskettes, compact disks for optical drives, magnetic tape, and othersas will occur to those of skill in the art. Persons skilled in the artwill immediately recognize that any computer system having suitableprogramming means will be capable of executing the steps of the methodas embodied in a computer program product. Persons skilled in the artwill recognize also that, although some of the example embodimentsdescribed in this specification are oriented to software installed andexecuting on computer hardware, nevertheless, alternative embodimentsimplemented as firmware or as hardware are well within the scope of thepresent disclosure.

Embodiments can include be a system, a method, and/or a computer programproduct. The computer program product may include a computer readablestorage medium (or media) having computer readable program instructionsthereon for causing a processor to carry out aspects of the presentdisclosure.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present disclosure may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, or either source code or object code written in anycombination of one or more programming languages, including an objectoriented programming language such as Smalltalk, C++ or the like, andconventional procedural programming languages, such as the “C”programming language or similar programming languages. The computerreadable program instructions may execute entirely on the user'scomputer, partly on the user's computer, as a stand-alone softwarepackage, partly on the user's computer and partly on a remote computeror entirely on the remote computer or server. In the latter scenario,the remote computer may be connected to the user's computer through anytype of network, including a local area network (LAN) or a wide areanetwork (WAN), or the connection may be made to an external computer(for example, through the Internet using an Internet Service Provider).In some embodiments, electronic circuitry including, for example,programmable logic circuitry, field-programmable gate arrays (FPGA), orprogrammable logic arrays (PLA) may execute the computer readableprogram instructions by utilizing state information of the computerreadable program instructions to personalize the electronic circuitry,in order to perform aspects of the present disclosure.

Aspects of the present disclosure are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to some embodimentsof the disclosure. It will be understood that each block of theflowchart illustrations and/or block diagrams, and combinations ofblocks in the flowchart illustrations and/or block diagrams, can beimplemented by computer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present disclosure. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the block may occur out of theorder noted in the figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

Readers will appreciate that the steps described herein may be carriedout in a variety ways and that no particular ordering is required. Itwill be further understood from the foregoing description thatmodifications and changes may be made in various embodiments of thepresent disclosure without departing from its true spirit. Thedescriptions in this specification are for purposes of illustration onlyand are not to be construed in a limiting sense. The scope of thepresent disclosure is limited only by the language of the followingclaims.

What is claimed is:
 1. A method of data transformation caching in anartificial intelligence infrastructure that includes one or more storagesystems and one or more graphical processing unit (‘GPU’) servers, themethod comprising: identifying, in dependence upon one or more machinelearning models to be executed on the GPU servers, one or moretransformations to apply to a dataset; generating, in dependence uponthe one or more transformations, a transformed dataset; storing, withinone or more of the storage systems, the transformed dataset; receiving aplurality of requests to transmit the transformed dataset to one or moreof the GPU servers; and responsive to each request, transmitting, fromthe one or more storage systems to the one or more GPU servers withoutre-performing the one or more transformations on the dataset, thetransformed dataset.
 2. The method of claim 1 wherein generating, independence upon the one or more transformations, a transformed datasetfurther comprises generating, by the storage system in dependence uponthe one or more transformations, transformed dataset.
 3. The method ofclaim 1 wherein transmitting, from the one or more storage systems tothe one or more GPU servers without re-performing the one or moretransformations on the dataset, the transformed dataset furthercomprises transmitting the transformed dataset from the one or morestorage systems directly to application memory on the GPU servers. 4.The method of claim 3 wherein transmitting the transformed dataset fromthe one or more storage systems directly to application memory on theGPU servers further comprises transmitting the transformed data datasetfrom the one or more storage systems to the GPU servers via remotedirect memory access (‘RDMA’).
 5. The method of claim 1 furthercomprising executing, by one or more of the GPU servers, one or moremachine learning algorithms associated with the machine learning modelusing the transformed dataset as input.
 6. The method of claim 1 furthercomprising: scheduling, by a unified management plane, one or moretransformations for one or more of the storage systems to apply to thedataset; and scheduling, by the unified management plane, execution ofone or more machine learning algorithms associated with the machinelearning model by the one or more GPU servers.
 7. The method of claim 1further comprising providing, by a unified management plane to the oneor more GPU servers, information describing the dataset, the one or moretransformations applied to the dataset, and the transformed dataset. 8.An artificial intelligence infrastructure that includes one or morestorage systems and one or more graphical processing unit (‘GPU’)servers, the artificial intelligence infrastructure configured to carryout the steps of: identifying, in dependence upon one or more machinelearning models to be executed on the GPU servers, one or moretransformations to apply to a dataset; generating, in dependence uponthe one or more transformations, a transformed dataset; storing, withinone or more of the storage systems, the transformed dataset; receiving aplurality of requests to transmit the transformed dataset to one or moreof the GPU servers; and responsive to each request, transmitting, fromthe one or more storage systems to the one or more GPU servers withoutre-performing the one or more transformations on the dataset, thetransformed dataset.
 9. The artificial intelligence infrastructure ofclaim 8 wherein generating, in dependence upon the one or moretransformations, a transformed dataset further comprises generating, bythe storage system in dependence upon the one or more transformations,transformed dataset.
 10. The artificial intelligence infrastructure ofclaim 8 wherein transmitting, from the one or more storage systems tothe one or more GPU servers without re-performing the one or moretransformations on the dataset, the transformed dataset furthercomprises transmitting the transformed dataset from the one or morestorage systems directly to application memory on the GPU servers. 11.The artificial intelligence infrastructure of claim 10 whereintransmitting the transformed dataset from the one or more storagesystems directly to application memory on the GPU servers furthercomprises transmitting the transformed data dataset from the one or morestorage systems to the GPU servers via remote direct memory access(‘RDMA’).
 12. The artificial intelligence infrastructure of claim 8wherein the artificial intelligence infrastructure is further configuredto carry out the step of executing, by one or more of the GPU servers,one or more machine learning algorithms associated with the machinelearning model using the transformed dataset as input.
 13. Theartificial intelligence infrastructure of claim 8 wherein the artificialintelligence infrastructure is further configured to carry out the stepsof: scheduling, by a unified management plane, one or moretransformations for one or more of the storage systems to apply to thedataset; and scheduling, by the unified management plane, execution ofone or more machine learning algorithms associated with the machinelearning model by the one or more GPU servers.
 14. The artificialintelligence infrastructure of claim 8 wherein the artificialintelligence infrastructure is further configured to carry out the stepof providing, by a unified management plane to the one or more GPUservers, information describing the dataset, the one or moretransformations applied to the dataset, and the transformed dataset. 15.An apparatus for data transformation offloading in an artificialintelligence infrastructure that includes one or more storage systemsand one or more graphical processing unit (‘GPU’) servers, the apparatuscomprising a computer processor, a computer memory operatively coupledto the computer processor, the computer memory having disposed within itcomputer program instructions that, when executed by the computerprocessor, cause the apparatus to carry out the steps of: identifying,in dependence upon one or more machine learning models to be executed onthe GPU servers, one or more transformations to apply to a dataset;generating, in dependence upon the one or more transformations, atransformed dataset; storing, within one or more of the storage systems,the transformed dataset; receiving a plurality of requests to transmitthe transformed dataset to one or more of the GPU servers; andresponsive to each request, transmitting, from the one or more storagesystems to the one or more GPU servers without re-performing the one ormore transformations on the dataset, the transformed dataset.
 16. Theapparatus of claim 15 wherein generating, in dependence upon the one ormore transformations, a transformed dataset further comprisesgenerating, by the storage system in dependence upon the one or moretransformations, transformed dataset.
 17. The apparatus of claim 15wherein transmitting, from the one or more storage systems to the one ormore GPU servers without re-performing the one or more transformationson the dataset, the transformed dataset further comprises transmittingthe transformed dataset from the one or more storage systems directly toapplication memory on the GPU servers.
 18. The apparatus of claim 15further comprising computer program instructions that, when executed bythe computer processor, cause the apparatus to carry out the steps of:scheduling, by a unified management plane, one or more transformationsfor one or more of the storage systems to apply to the dataset; andscheduling, by the unified management plane, execution of one or moremachine learning algorithms associated with the machine learning modelby the one or more GPU server.
 19. The apparatus of claim 15 furthercomprising computer program instructions that, when executed by thecomputer processor, cause the apparatus to carry out the step ofproviding, by a unified management plane to the one or more GPU servers,information describing the dataset, the one or more transformationsapplied to the dataset, and the transformed dataset.
 20. The apparatusof claim 15 further comprising computer program instructions that, whenexecuted by the computer processor, cause the apparatus to carry out thestep of executing, by one or more of the GPU servers, one or moremachine learning algorithms associated with the machine learning modelusing the transformed dataset as input.